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Guidelines for Condition Based Maintenance

T. Tinga (NLDA)

D. Soute (Wärtsilä)

H. Roeterink (Gasunie)

version 2.0

23 February 2010



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This report presents the results of a collaborative research project executed within the World

Class Maintenance Consortium.

The contents have been reviewed by:

P. Casteleijn Stork Asset Management Solutions

L. Pijpker Stork Asset Management Solutions

H. T. Lijzenga Koninklijke Marine



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Contents

List of abbreviations and definitions ...............................................................................................................4 1 Introduction ............................................................................................................................................5

2 Maintenance concepts............................................................................................................................6

3 Criteria for applicability of CBM..............................................................................................................8

3.1 General requirements.......................................................................................................................8

4 Proven CBM systems.............................................................................................................................10

4.1 Vibration monitoring.......................................................................................................................10

4.2 Condition monitoring of bearings ...................................................................................................10

4.3 Monitoring of hydraulic and lubrication systems ...........................................................................10

4.4 Condition monitoring based on process data .................................................................................10

5 Reliability and Maintenance Engineering tools.....................................................................................12

5.1 Maintenance strategy selection......................................................................................................12

5.1.1 Failure mode, effect and criticality analysis (FMECA)............................................................13

5.1.2

Reliability Centred Maintenance (RCM) ................................................................................13

5.1.3 Risk Based Inspections (RBI) ..................................................................................................13

5.1.4 Maintenance interval determination ....................................................................................13

5.2 Maintenance program execution....................................................................................................14

5.3 Evaluation and prognostics.............................................................................................................14

5.3.1 Updating (fixed) intervals ......................................................................................................14

5.3.2 Prognostics ............................................................................................................................15

5.4 Optimization....................................................................................................................................15

5.4.1 Example: quantifying the (financial) benefit of CBM.............................................................16

5.5 Software packages ..........................................................................................................................17

5.5.1 Benchmark.............................................................................................................................18

5.5.2 Maintenance modelling and optimization.............................................................................18

5.6

Sensors ............................................................................................................................................18 5.7 Future developments......................................................................................................................19

6 Guidelines for Condition Based Maintenance ......................................................................................20

7 Case studies ..........................................................................................................................................22

7.1 Description of the Gasunie pilot .....................................................................................................22

7.1.1 Gasunie selection process .....................................................................................................22

7.1.2 SRCM study of the compressor installation...........................................................................23

7.2 Description of the Wärtsilä pilot .....................................................................................................24

7.2.1 Motivation to apply condition based maintenance...............................................................24

7.2.2 CMS development using FMECA and RAMS..........................................................................25

7.2.3 CMS development using RCM ...............................................................................................30

7.2.4 Condition Based Maintenance...............................................................................................31

8 Validation of the guidelines ..................................................................................................................34

8.1

Validation of the Wärtsilä pilot .......................................................................................................34

8.1.1 Is the asset suitable for CBM ? ..............................................................................................34

8.1.2 Development of the customized system...............................................................................36

8.1.3 Conclusion Wärtsilä pilot.......................................................................................................37

8.2 Validation of the Gasunie pilot........................................................................................................37

8.3 Validation of the guidelines ............................................................................................................37

9 Conclusions ...........................................................................................................................................38

References.....................................................................................................................................................39



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List of abbreviations and definitions

Abbreviations

CBM Condition Based Maintenance

CMS Condition Monitoring System

CMMS Computerized Maintenance Management System

FMECA Failure Mode, Effect and Criticality Analysis

PGB Propeller Gearbox

PM Preventive Maintenance

RBD Reliability Block Diagram

RBI Risk Based Inspection

RCM Reliability Centered MaintenanceSRCM Streamlined Reliability Centered Maintenance

Definitions

Condition Monitoring System (CMS): is a real-time machinery parameter measurement &

signal processing system.

Condition Based Maintenance (CBM): constitutes a set of maintenance processes and

capabilities derived from real-time assessment of the machinery condition obtained from

embedded sensors and on-line signal processing.

Prognostics: is an automated CBM system, which predicts the remaining machinery service

life under defined operational conditions. Offline CMS: CMS, whereby the measurements and data collection are manual performed

(not automatically) by an engineer on regularly basis with a hand held measurement device

Online CMS: CMS, whereby the measurements and data collection are performed

automatically on a continuous basis without human intervention. The systems are

connected with a local network.

Online with remote access: Thruster and operational parameters can be monitored and

analyzed by experts on different locations.



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1 Introduction

In the First round of WCMC projects, Wärtsilä and Gasunie started the development of a

Condition Monitoring System (CMS) for one of their assets to be able to perform Condition

Based Maintenance (CBM). These activities have been continued in work package 4 during the

second round of WCMC projects. Alongside these practical pilot projects, some effort has been

put in the development of a set of general guidelines for the development of a CBM system.

During that task, the experience gained by Wärtsilä and Gasunie has been combined with the

more general knowledge of the other contributing partners (NLDA, Nedtrain and Stork AMS) and

several external partners (Rijksuniversiteit Groningen, TU Delft, Royal Netherlands Navy). The

result of these activities are reported in this document.

Chapter 2 provides a short introduction in maintenance concepts. Chapter 3 describes the

criteria that should be considered to decide whether a certain asset is suitable for CBM. Both

economical and technical issues are treated. In Chapter 4 a number of proven and commercially

available condition monitoring systems are described. Chapter 5 provides an overview of the

available tools for developing a suitable maintenance strategy and for analyzing reliability,

availability and costs. In Chapter 6 the main result of the present project is presented in the

form of a decision scheme that assists in deciding whether an asset is suitable for CBM and in

developing a CMS. In Chapter 7, the two pilot projects as run by Wärtsilä and Gasunie are

described and in Chapter 8 the decision scheme is applied to these pilots to assess the followed

development process. This also yields a validation of the presented decision scheme. Finally,

Chapter 9 provides some concluding remarks.



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2 Maintenance concepts

The main goal of performing maintenance is to obtain the optimal availability of a capital asset,which requires balancing the availability of the asset with the costs of maintenance activities

and non-productive periods (due to failure or maintenance). Performing too much maintenance

results in high costs and frequent non-productive periods, whereas too limited maintenance

leads to unexpected failures and reduced availability. There is a wide variety of maintenance

concepts that can be applied to maintain an asset. These concepts can be divided into two basic

types:

corrective maintenance: maintenance activities (repair, replacement) are only performed

when failure occurs. In that way, the service life of a part or component is fully utilized.

preventive maintenance: aims to prevent failure by performing appropriate maintenance

activities. The planning of those activities depends on either calendar time, the usage (or the

resulting load) or the condition of the system, where several subtypes of the concept are

available.

The maintenance activities in the latter preventive concept can be triggered by the following

quantities:

calendar time or age based: every day, week, month, etc.

usage based: triggered by either a usage parameter (operating hours, start / stops, km /

miles, etc.) or a load parameter (temperature, strain (magnitude or cycles), time, electric

current).

condition based: triggered by either a performance parameter (flow (in pumps), delivered

power / thrust, speed) or the physical condition (crack length, number of particles in

hydraulic oil, delamination in composite materials, vibrations (bearings)).

Which maintenance strategy is chosen depends on the criticality of the part and the variability

and predictability of the usage:• non-critical parts: corrective maintenance is in many cases the optimal strategy, since the

full service life is utilized and no expensive inspections or monitoring are required.

• critical parts: failure must be prevented for safety (e.g. aircraft, nuclear plants) or

economical reasons (large plants in process industry), so a preventive maintenance concept

is most suitable.

• constant usage: the usage is fully known and replacement intervals can be determined

accurately, e.g. for machines that are operated continuously at constant and well-known

power settings. A calendar time based concept is a cost-effective strategy in this case, since

no monitoring is required.

• variable usage: for systems that are operated in a more variable way (e.g. aero-engines,

thrusters) the usage must be taken into account when the service intervals are determined.Assuming the usage in the design phase is difficult, and therefore often large safety factors

must be applied. However, when the usage or load are monitored, a much more accurate

prediction of the service interval can be made. The only remaining uncertainty is the relation

between the usage / loads and the failure mechanism.

Therefore, the optimal amount of maintenance, which provides the best balance between costs

and availability, is ideally obtained with a condition based concept, since it enables the

execution of maintenance activities exactly on the right moment. However, not every asset is

suitable for a condition based maintenance concept. It must be feasible to assess the condition

of the asset and to relate the condition to the required maintenance activities. For many assets

that is not possible and a usage or load based approach may then be a better option. To



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determine whether a certain asset is suitable for a condition based maintenance concept, this

document provides a number of guidelines.



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3 Criteria for applicability of CBM

To decide whether an asset is suitable for CBM, several aspects have to be checked. This chapterwill discuss the criteria that play a role in this process, ultimately resulting in a decision scheme

(chapter 6). In the next chapter an overview of available reliability and maintenance engineering

tools is provided that can assist in completing the decision scheme. Chapter 4 provides an

overview of established CM techniques that already have been applied extensively in practice.

3.1 General requirements

In a general case, the following requirements can be identified for the suitability of an asset for

condition based maintenance:

1. technical requirements:

1.1. is it possible to identify the quantities that govern the maintenance needs of the asset

(i.e. the critical failure mechanism and the associated condition parameter) ?1.2. can these quantities be measured ?

1.3. can the (values or trends in) measured quantities be used to (timely) predict failures or

be translated into maintenance intervals ?

The latter question concerns the prognostic part of the CBM methodology, which in most cases

is the most difficult aspect. Just waiting until a certain parameter exceeds a critical value means

that at that moment short term action is required, which is difficult to plan (e.g. personnel,

spare parts) and may have serious consequences for the system availability. This is called the P-F

interval, the time between the detection of an expected failure and the actual occurrence of

that failure. Only when the P-F interval is sufficiently large, CBM is feasible.

2. economic/ safety requirement :

2.1. does application of CBM yield any financial advantage (lower maintenance costs, higher

availability) or does it increase the safety level ?

All these questions require a positive answer. If there is no economical or safety advantage, or

one of the required quantities cannot be measured, the asset is not suitable for CBM. On the

other hand, when all these question can be answered positively, the asset is suitable to be

maintained on a condition basis. The next step is then to decide how the CBM system can be

realized. That requires detailed answering of the following (technical) questions.

3. realization of the CBM system

3.1. is the condition assessed directly (wear, vibrations) or indirectly (performance) ?

3.2. what is the best method to measure the required quantities ?

is a suitable sensor available ?

is the location accessible ?

is data collection possible (local, remote/on-line) ? what will be the sample frequency (real-time or during regular inspections) ?

3.3. how can the (values or trends in) measured quantities be translated into maintenance

intervals ( prognostics) ?

Questions 3.1 and 3.3 are also addressed by using the typology proposed by researchers at the

Rijksuniversiteit Groningen [1] which classifies CBM methods according to two criteria:

• type of data used: process or failure data

• method for obtaining the expected value or trend: statistical or analytical modelling

The statistical modelling in this case refers to statistical and probabilistic methods that use

collected historical process or failure data to detect trends or extrapolate into the future in



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order to predict failures. Analytical models represent the more physical models that relate the

(measured) usage to predictions of service life, e.g. based on the failure mechanisms involved.

In most cases, the general requirements can only be checked when the more detailed questions

have been answered. Moreover, determining whether there will be an economical advantage is

in most case quite difficult. Therefore, in chapter 5 an overview of tools will be given that can

assist in these analyses.

The process described here is based on answering several questions. It results in a detailed

analysis of all aspects of a CBM system, which enables a proper judgement of the suitability of a

certain asset for CBM and yields useful guidelines for the actual development of the CBM

system. In practice, however, companies often don’t want to spend the time on a detailed

analysis and take quick decisions that are not based on detailed knowledge of the system failure

behaviour. This may result in the development of an expensive CBM system, that monitors the

wrong quantities and therefore only has a limited value. The guidelines developed in this project

may assist in limiting the effort (and costs) of performing a detailed analysis, on which a useful

CBM system can be based.In the last decades, a large number of CBM techniques has been developed and applied in

practice on a range of different systems. Some of these methods have proven to be effective

and to be generally applicable. These methods are in most cases available as commercial

services. Therefore, when an asset appears to be suitable for CBM, it may be possible to use a

commercially available CM system in stead of developing a new system. In the next chapter an

overview is given of proven CBM systems.



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4 Proven CBM systems

This section describes several CBM techniques [2] that have been applied in practice and haveproven to be effective. Most of these methods are available as commercial services. For all

techniques, the general requirements given in the previous section are checked, by addressing

the numbered questions (Q). Note that this overview is far from complete and several other

CBM techniques exist. However, some commonly applied methods are discussed here,

especially to illustrate how they satisfy the requirements presented in the previous chapter.

4.1 Vibration monitoring

Vibration monitoring is nowadays a widely accepted CBM method that can be used to detect

wear, failures or malfunctioning of rotating or reciprocating machinery. With regard to the

general requirements for CBM systems (section 3.1), this technique is based on the known

relation between measured vibrations and the failure mechanisms that are to be detected.

Vibration sensors, that measure displacements, velocities or accelerations, monitor the dynamic

behaviour of the system (Q1.1, 1.2). The resulting spectra can be used to detect, for example,

unbalance, misalignment, bearing wear or gear defects (Q1.3). The possibilities of assessing the

condition of the system depend on the complexity of the system, the number of sensors and the

availability of information on the machine or system (e.g. stiffness). For rather simple systems

like bearings, the method works quite well, as will be discussed next. For more complex systems,

analyzing trends in vibration characteristics may indicate a degradation of the system, but

quantitative predictions of remaining life may be hard to determine (Q1.3).

4.2 Condition monitoring of bearings

Since bearings are rather simple systems and also very widely applied in large numbers, the

system knowledge of bearings is well developed. As a consequence, the relation betweenmonitored vibrations and bearing wear or failures are known for the majority of the applied

bearings (Q1.3 / 3.3). This means that a CM system can be effectively used to optimize the

maintenance tasks on bearings (Q2.1) and a lot of bearing manufacturers can deliver sensors

(Q1.2) and analysis software with their products. The software is able to detect the type of

degradation and also to predict a value for the remaining life [3].

4.3 Monitoring of hydraulic and lubrication systems

Another well developed condition monitoring technique is the monitoring of hydraulic and

lubrication oil. By analysing the oil, both information about the quality of the oil and the

condition of the system can be obtained. The quality of the oil is quantified by determination of

the viscosity, the total acid / base numbers and the concentration of additives. Further,contamination of the oil may indicate a degradation of the system. The most important

contaminations are metal particles, originating from wear and corrosion processes (Q1.3), and

water [4].

In most cases, samples must be taken from the oil, which are then analysed in a suitable

laboratory. However, recently also oil monitoring sensors (Q1.2) have been developed that may

be used to measure the water content in oil or to count the number of particles.

4.4 Condition monitoring based on process data

In complex installations it is difficult to monitor directly all relevant failure mechanisms.

However, by monitoring the performance of the system, it is in many cases possible to detect



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incipient failures (Q1.3). This type of CM is applied in e.g. gas turbines, marine refrigeration

plants [5], and installations in the process industry. Typically, data from large numbers of

sensors measuring quantities like (gas or fluid) pressure , mass flow and temperature at

different locations is collected (Q1.2). Suitable data mining techniques are then used to discover

trends in the datasets that could be used to predict failures or deterioration of the system (Q1.3

/ 2.1). Contrary to the methods discussed before, in this case no direct relation between a

physical failure mechanism and the associated condition parameters is available (Q3.1, 3.4).



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5 Reliability and Maintenance Engineering tools

If a new asset is designed, a suitable maintenance strategy must be developed. The execution of this strategy must be started as soon as the asset is in service and the program should be

evaluated periodically. Simultaneously, simulations of the maintenance process could assist in

optimizing the strategy by comparing different scenario’s. This process representing the

maintenance strategy life cycle is shown schematically in Figure 5-1. In this chapter the four

phases, strategy selection, execution, evaluation and optimization, will be described and the

available tools that can assist in these activities will be discussed. Also, as an example of

optimization, the application of the tools to quantify the (financial) benefits of CBM for a certain

asset is discussed. In industry, the strategy selection is typically performed by a maintenance

engineer, while the evaluation and optimization activities are performed by reliability engineers.

Figure 5-1 Schematic representation of the maintenance strategy life cycle.

Note that this chapter provides a general description of the maintenance program development,

not only focussing on condition based maintenance. The reason is that many asset will appear to

be unsuitable for CBM, and the alternative approaches are then available in this section. After

the description of the methods, an overview of available software packages that apply these

methods is given. Finally, an overview of available sensor types is given. The information

provided in this chapter will serve as background knowledge for the decision scheme presented

in the next chapter.

5.1 Maintenance strategy selectionThe first step in developing a maintenance program is the selection of an appropriate

maintenance strategy. This implies that a choice has to be made for either corrective or

preventive maintenance, as was explained in chapter 2. Further, when a preventive strategy is

selected, the trigger for the maintenance activities has to be defined: a fixed time interval (age

or calendar time based), a certain amount of operating hours (usage based) or at a certain

condition (condition based). This selection process can be supported by tools like FMECA, RCM

and RBI, as will be explained below. Moreover, when a preventive strategy with fixed or usage

based intervals is selected, the length of the maintenance or inspection intervals has to be

determined.



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5.1.1 Failure mode, effect and criticality analysis (FMECA)

A complex system normally contains a large number of components. The maintenance needs

are governed by the most critical component(s). Moreover, these critical components may besubject to several different failure mechanisms, whereas only one of these mechanisms is life-

limiting. It is therefore important to determine which component and which failure mechanism

are critical for the system under consideration. The Failure Mode, Effect and Criticality Analysis

(FMECA) is a tool that supports this task. It assists in performing a structured breakdown of the

system, determination of the failure mechanisms and quantification of the associated risks [6]. A

lot of commercial software tools are available for FME(C)A analyses (see also 5.5).

5.1.2 Reliability Centred Maintenance (RCM)

The Reliability Centred Maintenance (RCM) concept has it roots in the late 1960s in the aircraft

industry, and was proposed in its final form in 1978. Nowadays, the approach is also applied in

sectors other than the aerospace industry, like the automotive, energy, naval [6] andmanufacturing industries. RCM is a systematic approach for selecting applicable and effective

preventive maintenance tasks for each item in a system taking into consideration failure

consequences [7]. The previously discussed FMECA is actually an integral part of the RCM

approach, assisting in determination of failure modes and their effects. Ultimately, the RCM

approach yields a decision scheme that can be used to select the appropriate maintenance task

for each individual component in a system.

Since the presentation of the original RCM concept, several variants have been developed. The

RCM-2 method starts with a criticality analysis of the system and only critical components are

considered in the subsequent RCM study. This reduces the effort considerably. A very similar

approach is followed in the SRCM method as applied by SKF.

A large number of software tools are available to assist in performing an RCM analysis and the

method is also integrated in the large reliability analysis packages (see 5.5).

5.1.3 Risk Based Inspections (RBI)

The purpose of the risk based inspection (RBI) methodology is the development of a cost-

effective maintenance and inspection program. This methodology is typically applied to static

equipment. The probability of failure and its consequences (risk) are used to prioritize the

inspections of all components in a system. This method is widely applied in the oil and gas

industry and mainly focuses on corrosion processes in pipework. Planned inspections are

generally performed using non-destructive inspection (NDI) techniques.

5.1.4 Maintenance interval determination

When a predictive strategy is selected, the next issue is the determination of the appropriate

maintenance intervals that should be used. As was mentioned in chapter 3, this can be done by

applying either statistical methods or by using physical failure models.

Statistical methods

When maintenance experience on similar systems is available, for example when failure data

has been collected, that information can be used to estimate the appropriate maintenance

intervals for the new system. If there is no experience on the system level, there may be data

available on a component level, since similar components (e.g. bearings, gears) are often used in

a range of systems. The statistics of the historical data can then be used to determine the



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expected mean time between failures (MTBF) and its variation. If no data is available at all,

expert knowledge can be used to estimate the MTBF. A best practice estimation1

of the variation

of the MTBF appears to be ± 25%. Note that the estimated MTBF is now based on historical data

and the associated usage. If the usage of a system differs significantly from the past usage, it is

hard to make a good estimate for the MTBF.

Physical models

An alternative way to determine the maintenance interval is to use physical failure models.

These models enable the prediction of the component service life for a given component

loading. Since the component loads are related to the usage of the system, detailed system

knowledge allows the calculation of these loads. The corresponding service life can be calculated

when the governing failure mechanism is known and a quantitative failure model is available. In

this way, the assumed usage of the system can be translated into a MTBF [8]. The variation in

MTBF depends on the one hand on variations in usage and on the other hand on variations in

material properties and component dimensions. The effect of these variations on the MTBF can

be quantified with stochastic analyses (e.g. Monte Carlo simulations). Using physical models

implies that the relation between usage and service life is quantified. Therefore, changes in

usage can easily be taken into account when calculating the expected MTBF.

5.2 Maintenance program execution

As soon as the asset enters service, the execution of the developed maintenance program must

be started. In addition to execution of the different maintenance tasks (repair, replacement,

inspection) it is very useful to collect relevant maintenance and inspection data. This data, e.g.

component failure times or inspection results, can be used to evaluate and update the

maintenance program, as will be discussed in the next section.

The structured collection of the data and well-organized storage of the information in aComputerized Maintenance Management System (CMMS) is very important. A lot of these

CMMS software packages are available nowadays and they are widely used by the larger

companies. When a condition monitoring system is operative, the amount of collected data is

much larger, since many CM systems monitor their system real-time at rather high sampling

rates. In this case it is even more important to make good arrangements for the data storage,

preferably in a suitable CMMS. Several commercial CMM systems enable the direct connection

with a CM system.

5.3 Evaluation and prognostics

Once a certain amount of maintenance data has been collected, analysis of the data to evaluate

the selected maintenance program is required. When fixed preventive maintenance intervalsare used, the gathered data can be used to update the length of the intervals that have been

estimated during the design. In case of condition based maintenance, the collected condition

data must be used to perform prognostics: a prediction for the remaining time to any

maintenance activity must be made.

5.3.1 Updating (fixed) intervals

For the first issue, updating the used intervals, several statistical methods are available. In

general, these methods determine the characteristics of the data set by fitting a statistical

1Stork AMS presentation on reliability and maintenance engineering tools, 6 November 2009, Drunen.



15

distribution, e.g. a Weibull, normal or lognormal distribution, to the data. The distribution

parameters are calculated and the goodness-of-fit is quantified. The resulting distributions can

be applied to determine the expected average failure time (MTBF) or the failure time associated

to a certain probability of failure. These results can be used to update the maintenance

intervals. In case of a Weibull analysis, the shape of the distribution function also indicates the

type of failure behaviour, since different values of the shape parameter are associated to infant

mortality, constant failure rate and wear-out behaviour.

Another way to validate the used intervals is the application of condition assessments, in which

the physical condition of the replaced parts or components is checked. This condition

assessment provides an indication for the remaining life of the component, which may be used

to update the replacement intervals. Similarly, a Root Cause Analysis (RCA) of failed parts yields

insight in the failure mechanisms and processes, which also provides valuable information to

validate or modify the applied intervals.

5.3.2 Prognostics

For condition based maintenance, the second issue of the prognostics is very important. This can

again be approached in two different ways: statistically and by physical modelling. The statistical

approach consists of checking the trends in the failure behaviour of components as described by

the statistical distributions. The Reliability Growth method is used to quantify changes in

performance (e.g. in terms of MTBF) in time, for example due to changes in usage profile or

improvements in the maintenance strategy. By extrapolating these trends into the future, an

estimate of expected failure times (prognosis) can be obtained. These type of methods are

widely used in vibration analysis to predict remaining lives for components like bearings, where

the trends in vibration patterns or levels are extrapolated into the future.

Alternatively, physical failure models can be applied to predict the remaining life of a

component. As was explained in 5.1.4, this enables quantifying the relation between usage anddamage accumulation. Compared to the statistical prognostic methods, there are two important

advantages. Firstly, the prognosis is not based on historical data only, which means that changes

in usage or operational conditions can be taken into account. And secondly, the uncertainty in

the prediction of the remaining life (caused by variations in usage, materials and dimensions)

can be reduced significantly by including their effect in the failure model. This means that much

less conservative intervals can be used, which largely increases the efficiency of the

maintenance process, as was shown in a recent project [8].

5.4 Optimization

The final step in finding an optimal maintenance strategy is the use of modelling and simulation

techniques to compare different scenario’s. This step can be done in parallel with the previousthree steps, although the benefit is larger when more data is available. The optimization process

can be done at several levels of complexity, ranging from manually comparing a small number of

different scenario’s to finding the real optimum under several constraints using sophisticated

mathematical optimization algorithms. Quantities that are often optimized or used as

constraints (minimal, maximum allowable values) are reliability, availability and costs.

The manual comparison implies the definition of a small number of scenario’s which are

expected to improve the process efficiency. To be able to incorporate the statistical nature of

the failure processes, as characterized by the distribution functions, a Monte Carlo method is

often applied to simulate the processes. This means that for each scenario a large number of

simulations is performed, for which the parameter values are randomly taken from the



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appropriate distributions. The result is a quantification of the reliability or availability of a

system and the associated costs, which enables comparison of the different scenario’s and

selection of the most suitable strategy.

In the research field of quantitative maintenance modelling, processes are mathematically

optimized in terms of costs or reliability. A large amount of methods, e.g. [9-11], have been

developed in the last decades for a wide variety of maintenance concepts (with or without

inspections, repair (full or minimal), component replacement, etc.). All these optimization

methods, both the manual and the full optimization methods, require two types of input:

quantification of the costs of all maintenance activities (repair, inspection, replacement,

down-time, etc.)

a quantitative description of the degradation process that is responsible for failure of the

system

The optimization yields values for the optimal replacement intervals, inspection intervals or

decides whether a component must be replaced or repaired. However, although the methods to

calculate the costs or to optimize a strategy are readily available, the largest problem is thecollection of useful input data. The methods will only provide a reliable solution when the costs

and degradation can be quantified accurately. In most cases, the costs of the different activities

can be obtained, but an accurate description of the degradation is often hard to define. The

latter is therefore often described by a stochastic process (e.g. gamma process [11], Poisson

process), for which the parameters have to be determined from measured service data. Since

that data is often hard to obtain or available in limited amounts, recently methods have been

proposed based on fuzzy logic [10]. However, the uncertainty in the prediction of the

degradation behaviour, associated to variations in usage and material properties, leads to

uncertainty in the predicted optimal strategy. As was mentioned before, this uncertainty could

be reduced considerably by replacing the statistical description of the failure behaviour by

physical failure models [8].Whereas optimizing the maintenance on a component level is rather straightforward, the

optimization on the system level (covering a large number of components) is more complex due

to the interaction of the reliability of several components on the overall system reliability. For

example, the application of redundancy in the system largely affects the system reliability, while

the component reliability is still the same. A useful tool to analyse the system reliability is

Reliability Block Diagram (RBD), which visualizes the component dependencies (serial or parallel)

and supports the calculation of the system reliability.

Without the precise knowledge of the degradation process, optimization in an absolute manner

is difficult. However, comparing different scenario’s for an assumed usage and associated

degradation behaviour is possible in many cases. In the context of the present document, that

would enable the comparison of maintenance strategies with and without condition monitoring,

as will be explained in the example below.

5.4.1 Example: quantifying the (financial) benefit of CBM

On deciding whether an asset is suitable for condition based maintenance, one of the most

important questions to answer is whether application of CBM provides a financial or safety

benefit. This question can be answered by comparing two scenario’s, one with and one without

condition monitoring, using the methods described above. This requires quantification of the

costs for the required maintenance activities, but also quantification of the financial benefits of

preventing failures. Application of a CBM system can yield cost advantages in two ways:



17

the standard maintenance intervals can be extended, since repair or replacement is

triggered by the actual condition of the system

unexpected failures can be prevented using the information about the system condition

A good indication of the potential cost advantage associated to interval extension can be

obtained by determining the remaining service life of the components at the moment they are

replaced (at the standard intervals). The benefit of preventing failures is governed by the repair

costs for the component and other resulting damage and by the costs of system down-time.

This can be simulated numerically by comparing the two scenario’s, using relevant numbers for

the frequency or the probability of an unexpected failure (Table 1) and the costs of all relevant

maintenance activities (see Table 2). Then a numerical description of the failure process is

required, either as a stochastic / statistical formulation or physical failure model. A Monte Carlo

analysis can then be performed to calculate the total costs at a required reliability level for both

scenario’s, which provides the quantification of the benefit of CBM.

Table 1 Costs and frequency of unexpected failures.

Event costs probability

unexpected failure replacement / repair, down-time, other

damage

pf

Table 2 Costs and intervals of maintenance activities.

Activity costs interval

component replacement spare part, replacement costs (labour

costs, consumables), down-time

I1

inspection labour costs, down-time I2 repair material, labour costs, down-time I3

Even when a detailed failure process description is not available and a simulation is thus not

feasible, an indication of the remaining service life of replaced components and global

comparison of the costs mentioned in Table 1 and Table 2 can provide a reasonable estimate of

the benefit of CBM.

5.5 Software packages

The methods described in the previous sections have been implemented in a large number of

commercial software codes. The main division that can be made in these codes is between

general purpose codes that include several tools and support the complete maintenance

program definition and execution, and specific codes that focus on only one of the analysis

tools, like FMECA or RCM. The general purpose codes are convenient, since all analyses can be

performed within the same code. Also, connections to condition monitoring (CM) or

maintenance management (CMMS) systems are often possible. On the other hand, the codes

for one specific analysis tool often provide more sophisticated options for that specific analysis

and may therefore be the appropriate choice for some detailed analyses. An overview of a large

number of codes is given on the Plant Maintenance Resource Center website2.

2http://www.plant-maintenance.com/



18

5.5.1 Benchmark

Stork AMS recently performed a benchmark on the general purpose software codes, focusing on

the following packages: Meridium APM, Meridium RCMO, IVARA EXP and Isograph Availabilityworkbench. The strong points of each of these codes is given below:

Meridium APM:

interfaces with various CMMS (e.g. SAP, MAXIMO, Datastream)

complete reliability analysis suite

can be build up in a modular way

extensive optimization options

Meridium RCMO

basic version of APM, only classical RCM and FMECA

low-level reliability analyses, limited optimization options

integration in SAP (add-on)

IVARA EXP Pro

interfaces with various CMMS (e.g. SAP, MAXIMO)

excellent Condition Monitoring feature

medium level reliability / optimization

Isograph Availability workbench

certified interface with SAP and MAXIMO

high level reliability

extensive simulation and optimization options

Recently, IVARA and Isograph decided to combine their activities in one code, which means thatIVARA EXP Pro now also enables high level reliability analyses and provides extensive simulation

and optimization options. However, note that the optimization in all these codes still consists of

comparing manually defined scenario’s. None of these codes enables the full (mathematical)

optimization of the maintenance strategy.

5.5.2 Maintenance modelling and optimization

Numerous scientific papers have been published in this field the last decades, providing several

optimization methods. These methods have not yet been implemented in the large general

purpose codes as discussed in the previous section. Practical application of these methods

therefore requires coding the equations in mathematical software packages. This can be done in

general codes like Excel or Matlab, but also more specialized packages like Entreprise Dynamicsare available. Also the use of physical failure models in reliability analyses is not yet common

practice. Therefore, also the application of these methods requires coding the models in

mathematical software packages, that may be connected to the reliability codes.

5.6 Sensors

The key aspect of any condition monitoring system is the collection of load or condition data.

This requires sensors that are able to measure the required quantities with sufficient accuracy. A

wide variety of sensors is available nowadays. The most commonly used sensor types are

described here, starting with a number of commonly used and dependable sensors:



19

vibration sensor: measures displacements, velocities or accelerations to monitor the

dynamic behaviour of a system; widely used in vibration monitoring systems for e.g.

bearings.

temperature sensor: measures temperature; wide variety of sensor types, e.g.

thermocouple.

pressure sensor: measures gas / fluid pressure;

strain gauge: measures deformation of a structural part; based on electrical resistance

measurement.

Some more sophisticated sensors, which have been developed more recently and have not been

applied as extensively as the above mentioned sensors:

oil sensor: monitors water content or particle content of oil to detect component wear or

seal degradation; rather new development, replaces laboratory analysis of manual oil

samples.

Fibre Bragg grating: optical fibre based sensor that quantifies straining of the fibre bydetecting changes in transmitted or reflected wave lengths; can also be used to construct

temperature and pressure sensors.

delamination sensor: detects delamination between different layers in composite materials;

can be integrated in structure.

crack length sensor: measures the length of cracks in a structure, usually based on electrical

resistance measurements.

In the last decades, structural health monitoring (SHM) is getting much attention. Sensors are

integrated into structures and enable the real monitoring of structural integrity. Especially

composite structures are very suitable for these techniques, since (fibre based) sensors can be

integrated into the structures rather easily during the manufacturing process.

5.7 Future developments

Reliability and maintenance engineering methods are traditionally based on statistical methods.

Moreover, the maintenance world is rather conservative, which means that existing methods

are still believed to be the best choice and innovation is limited. However, integration of existing

reliability methods with the physical principles of failure offers a large potential for maintenance

process improvements. Especially for prognostic methods, which are very important for

condition based maintenance, the knowledge of the failure mechanisms is essential. Therefore,

considerable (research) effort should be put in the development of innovative methods that

cross the traditional borders between reliability engineering and structural integrity modelling.



20

6 Guidelines for Condition Based Maintenance

Based on the general requirements for condition based maintenance that were discussed inchapter 3, a decision scheme has been developed that assists in deciding whether a certain asset

is suitable for CBM and in developing an appropriate condition monitoring system. The decision

scheme is shown in Figure 6-1 and requires answering the subsequent questions. For most of

the questions a reference to relevant information is added (left-hand column), where the

numbers refer to the sections in this report.



21

A1) possible to identify critical components, failuremechanism & associated condition parameters ?

A2) can these parameters be measured ?

A3) can measured quantities be translated intomaintenance intervals / failures ?

A4) does application of CBM yield financial

or safety benefit ?

A5) possible to implement CBM in organization ?

YES

NO

YES

YES

YES

NO

NO

NO

NO

A6) is commercial CBM system available ?

Decision scheme Condition Based Maintenance

FMECA ( 5.1.1 )RCM ( 5.1.2 )

Sensors available (5.6) ?Location accesible ?

Data collection possible ?

Prognostic or predictive

tools available ? ( 5.3 )

Proven methods (4)

B1) is condition assessed directly or indirectly ?

B2) what is best method to measure ?

B3) how to obtain maintenance intervals from data ?

B4) determine critical values for measuredparameters or trends

(3.1)

Selection of sensors,location, data collection,

frequency (3.1)

CMMS integration ?

Prognostic tools (5.3)

Trending (5.3)

Cost Optimization( 5.4 )

YES

NO

YES

A s s

e t N O T S U I T A B L E f o r C B M

Purchase & install

Develop customized systemconsider following aspects:

Information (section)

B5) are there any boundary conditions to consider ?

Legislation, authorities,

insurance, safety © WCMC - T.Tinga

Figure 6-1 Decision scheme Condition Based Maintenance. The bold printed numbers in the left

column refer to sections in this report.



22

7 Case studiesThe pilot projects executed by Wartsila and Gasunie will serve as case studies. Application of the

developed guidelines will be demonstrated.

7.1 Description of the Gasunie pilot

The Gasunie pilot is a research project to investigate in practice the possibilities of monitoring

techniques to determine the condition of a gas turbine compressor installation during

operation. These type of compressor installations often experience a lot of start-up and shut-

downs, depending on the actual gas transmission demand. Since October 2008 a selection of

existing machine signals are collected continuously for analysis. During the course of the pilot

project, the need to collect additional machine signals and other relevant process or

environment parameters would become clear. Before the pilot actually could begin, a selection

process was performed to select suitable assets of the Gasunie infrastructure for a Condition

Monitoring test. After that process led to the selection of a gas driven compressor installation, aStreamlined Reliability Centered Maintenance (SRCM) study was performed to define

appropriate maintenance tasks for different components.

7.1.1 Gasunie selection process

Gasunie has many assets and different types of assets. To judge whether CBM could be a

suitable maintenance strategy for an certain asset, four criteria have been checked [12]:

1. Applicability: is CBM the right maintenance strategy and are there applicable technologies?

- is the need for maintenance triggered by a continuous degradation process or merely by

unexpected events leading to an abrupt failure of the asset ?

- are monitoring techniques available to detect the condition of a system without

interfering its operation ?

2. Feasibility: can the maintenance strategy be implemented ?

- are resources available ?

- are the monitoring techniques safe enough ?

3. Improvement potential of the individual asset

- what is the improvement potential with regard to availability and reliability ?

- what is the expected reduction in life cycle costs ?

4. Impact on the whole business

- can the maintenance strategy for the individual asset be copied to other assets of the

same type or even to different type of assets ?

- will the better performance of this individual asset lead to a substantial better

performance of the gas transmission network ?

By checking these four aspects for potential assets, the suitability of the asset for the CBM pilot

was determined. That answers part of the questions mentioned in 3.1, but also some specific

issues were not addressed at this stage:

o which quantities govern the maintenance needs of the asset ?

o can the (values or trends in) measured quantities be translated into maintenance intervals?

Further, the economical advantage of applying CBM is checked, but the actual quantification of

this advantage remains difficult. This issue is also addressed in section 5.4.

As a result of the described process, a gas driven compressor installation has been selected for

the CBM pilot project.



23

7.1.2 SRCM study of the compressor installation

Gasunie has performed a Streamlined Reliability Centered Maintenance (SRCM) study lead by

SKF for the selected compressor installation. It provides a good indication for the requiredmaintenance tasks within a full time week study.

Explanation of the SRCM method

Streamlined Reliability Centered Maintenance (SRCM) is a relatively short RCM study. Objective

of the SRCM study is to define appropriate maintenance tasks for different components of a

system. Essentially three steps have to be taken:

- identify the important system functions

- define preventive maintenance tasks to be performed

- update the existing maintenance program

To define the preventive maintenance tasks, it is important to distinct critical components from

non-critical components. For critical components, preventive maintenance tasks should bedefined or a change in the design of the installation should be proposed. For non-critical

components, simple preventive maintenance tasks should be defined or Run-To-Failure should

be adopted.

To be able to make a distinction between critical and non-critical, business goals should be clear

as well as their translation to asset level in terms of costs, safety, reliability, availability and

sustainability. A Criticality Matrix helps to make a Risk Assessment of possible component

failures by estimating the failure frequency and the severity of the consequences of a failure.

Everything which has a high or medium risk score (red/orange sector) is considered as critical.

The low risk score (green) is judged as non-critical.

A multidisciplinary team (departments) should perform the SRCM study to cover all relevant

aspects and stages of the maintenance cycle. This Key Group should at least contain

representatives of Operations, Maintenance and Plant Engineering departments. A PlantManager and a Corporate Representative should be involved to support changes in the existing

maintenance procedures.

SRCM study of the compressor installation

Two subsystems of the compressor installation have been chosen for the SRCM analysis: the oil

system (seal oil and lubricants) and the fuel gas system. The maintenance of these subsystems is

completely done by Gasunie. There was lack of time and lack of knowledge to analyze the gas

generator subsystem, for which essential maintenance is performed by the OEM.

The boundaries of the two subsystems were set and the components were defined with help of

SAP, with PID's and with help of actual system knowledge of Operations and Maintenance

people. Each component (more than 80) for the both subsystems was analyzed with regard tofunction, failure modes, failure causes, criticality and maintenance tasks. The first day it turned

out to be a time-consuming process due to unfamiliarity with the SRCM process and due to

extensive discussions about the failure causes and criticality. SRCM is not intended to cover all

possible failure causes, but only the dominant ones. The analysis was facilitated with a SKF

SRCM™ software tool. It structured the process and the inputs and results could easily be found.

In total, 61 maintenance tasks have been defined for the two subsystems. The majority of these

tasks had a "mechanical" background. The SKF trainer thought the tasks with an "electrical"

background had not been represented enough. During this study week, the defined tasks were

not systematically compared with the existing maintenance tasks. However, in some cases the

maintenance engineers knew that some proposed inspection intervals of components differed



24

from the existing intervals. For several critical components condition monitoring was suggested

as a feasible maintenance task (two new) whereas some non-critical components were newly

categorized as run-to-failure.

7.2 Description of the Wärtsilä pilot

The Wärtsilä pilot concerns the development of a Condition Monitoring System for a steerable

thruster (see Figure 7-2) in order to perform Condition Based Maintenance. In this section the

pilot study concerning this development is described and especially the selection of the

parameters to be monitored is discussed. This work has been done within Wärtsilä in two

consecutive WCMC projects since 2007 [6, 13].

The first project in the first round of WCMC started with a literature research on vibration and

oil monitoring and an investigation on the wear behaviour of sliding bearings. Also the pilot on

the Condition Monitoring System for steerable thrusters was started with the selection of

sensors, wireless data transmission, data acquisition and remote access and the building and

testing of the wireless transmission system (disruptions, stability and the implementation of filters).

In the second project, the work on the pilot continued with the building of demonstrator used

to validate the online system and remote access. Also, the Condition Monitoring System

(wireless transmission, sensors…) was build into a real thruster. In the validation trial on the

demonstrator the wireless transmission, the attenuation of signals (accelerometers), oil

monitoring, the online system and the remote access were tested. In the next subsections the

process of selecting the appropriate monitoring parameters is described. This process has been

supported by using both a combination of FMECA and RAMS tools and the RCM method.

7.2.1 Motivation to apply condition based maintenance

Wärtsilä manufactures different kinds of thrusters. Most of the thrusters are fixed to the vesseland can only be accessed when the vessel is docked. This means that the thruster maintenance

intervals must be equal to the intervals in between to dockings, so for these thrusters condition

based maintenance does not yield any benefit. However, for the modular thruster it is possible

to choose an under water mountable thruster. Those thrusters are best applied on vessels that

are either too big to go into a dry-dock, or vessels that are intended to stay out of dry-dock for

periods longer than the (classification prescribed) docking intervals.

Figure 7-1 Underwater mountable thruster.

When a full inspection of the thruster is required, the mounting sequence shown in Figure 7-1

can be reversed, thus demounting the removable part. The removed part can then directly be



25

replaced by a spare removable part. The removable part can be overhauled either onboard or

after shipping it to the shore. Due to the flexibility in maintenance intervals, condition based

maintenance may be beneficial for this type of thruster. The CM system enables early detection

of faults. Based on the severity of the fault the decision can be made to exchange the removable

part of the thruster by the spare one that is onboard. In this way, there is no need to leave the

present job and sail to a docking place, which saves a lot of money. More precisely, with a

Condition Monitoring System the condition of main components of the propulsion units can be

monitored, resulting in:

• early detection of deterioration of components like bearings and gears

• reducing the risk of unplanned maintenance and dry-docking

• decreasing down time

• withdrawal or postponement of inspection intervals

Each of these aspects can save money and increase the operational performance of the system.

7.2.2 CMS development using FMECA and RAMS

In this section the development of the CM system is described. FMECA and RAMS tools are used

to select the appropriate condition parameters. Firstly the possible monitoring parameters in

the thruster are determined, then the critical components are identified and finally the failure

modes and their causes are assessed. The latter enables the definition of governing monitoring

parameters and their critical levels.

Identification of possible monitoring parameters

The development of the CM system started with the identification of possible measurements on

a steerable thruster. A lot of items can be technically measured:

• Device responses with normal sensors as installed (no additional sensors for CMS).

• Quality of lubricant

• Fatigue measurement by strain gauges

• Device performance, efficiency

• Device thermal behaviour: pressures, mass-flow and temperature

• Vibration and acoustic characteristics for wear-out.

Figure 7-2 shows a selection of thruster parameters that can be measured. The responses of the

device (for instance steering) will be provided by the controls of the steerable thruster. To

monitor the quality of the lubricant, water ingress via seals and additives or particles in the oil

can be measured by a saturation and a contamination sensor, respectively. The thermal

behaviour can be measured by two temperature sensors in the oil. Vibration characteristics for

wear-out of gears and bearings can be measured by six accelerometers placed near gears and

bearings. Vibration time signals can be measured and RMS trends and frequency spectra can begenerated.



27

from sub-functions (main units) to critical components. The blocks in the functional breakdown

are divided in three parts:

Function

Technical requirements to perform the function

The system/subsystem/assembly or component

The consequences of failure at local level, as well as at system level are investigated in a

(functional) failure mode and criticality analysis, FMECA. For each component/assembly in the

steerable thruster system at least one failure mode has been determined. Each failure mode

may have one or more failure causes. For each failure mode the local capacity by normal

operation on component level, the remaining local capacity by failure on component level and

the capacity by failure on system level is determined. The lubrication system, for example,

contains a redundant pump assembly with E-motor. The local capacity in normal operation is

100% (= flow), remaining local capacity by failure is 0% (= no flow) and the remaining system

capacity is 100% (= flow) because of the redundant pump assembly. The result of this FMECA is

displayed in Figure 7-4.

Figure 7-4 FMECA results for the thruster (confidential).

When focussing on the parts which are mostly maintained by only Wärtsilä the most important

subsystem and assemblies are:

• Upper gearbox (UGB): Seals and bearings

• Stemsection (Stem): Seals and steering motor assy

• Propeller gearbox (PGB): Seal assembly and bearings

The preventive maintenance costs (replacement) of seals and bearings is high. To perform

replacement of these parts the thruster must be taken out of the ship, which is a costly action. It

is also shown in Figure 7-4 that certain failure modes of the bearing, gear set and the steering

gear have a high criticality score in the performed FMECA. Based on the performed FMECA it

was decided to focus on the Propeller Gearbox (PGB).

Failure modes and required monitoring parameters

For each of these assemblies or components it is important to investigate what the failure

modes and failure causes are, how and where these causes can be measured, what guidelines

for the measured values are and how the combination of these measured parameters influences

the lifetime of each assembly or component. In Figure 7-5 an overview of the thruster with the



28

gear-set and the bearings is shown. The failure modes for the bearings, seals and gears are

discussed next.

Figure 7-5 Overview of the bearings and the gear-set of the thruster.

Bearings

The failure modes and mechanisms of bearings are summarized in Table 3. As can be observed,

the cause of bearing failure in both cases is a insufficient or bad lubrication. The cause of failure

and local effects can be detected by one of the three following parameters:

• Vibrations near the bearing

• Oil temperature

• Particles and water in oil

Therefore, these parameters will be monitored during operation and action will be taken when

one of these parameters exceeds a critical value. Guidelines to determine the critical value are

obtained from a maintenance manual and ISO standards. The monitored quantities are:

• Vibrations: RMS velocity

• Failure frequency bearing: natural frequency bearing, ball pass frequency inner ring (BPFI),

ball pass frequency outer ring (BPFO), ball spin frequency (BSF).

• Temperature

• Particles: contamination level

• Water: saturation level

Table 3 Failure mode bearings

Assembly/component

Failuremode

Failuremechanisms

Local EffectSystemeffect

Failurecauses

safeguards

BearingsSeizedbearing

Surfacefatigue

Noise from thruster(possible highertemperature oil)

Total loss ofpropulsion

Insufficient/ badlubrication

Temperature alarm onlubrication system, oil filter, oilsamples (water/particles)

Crackformation onrings and ballsor rollers

Vibration ofthruster (possiblehigher temperatureoil)


Insufficient/ badlubrication

Temperature alarm onlubrication system,oil filter, oil samples(water/particles), trendingvibration

When one of the monitored parameters exceeds the critical value, action must be taken.

However, determination of the time span that is available to perform that action still has to be



29

determined. This is the prognostics part of the CBM system, used to determine the remaining

life (see section 7.2.4 and Figure 7-6).

Seals of the propeller gearbox (PGB)

The failure modes and mechanisms of seals are summarized in Table 4. As can be observed the

cause of failure for each component of the seal assembly is inadequate lubrication or

contaminants (ingress of debris). The cause can not be detected directly by sensors but the local

effects and failure mode can be detected by the following parameters:

• Water in oil

• Oil level in monitoring tank


one of these parameters exceeds a critical value.

Table 4 Failure modes for the PGB seal.

Assembly/component

Failuremode

Failuremecha-nisms

Local Effect System effect Failure causes safeguards

Seal rings Leakage Wear Loss of oil,pollution,water ingress

No direct system effectwhen leakage is not toomuch, but repairs areneeded

Inadequate lubrication,Contaminants (ingressof debris)

Monitoring tank,saturation andcontaminationsensor

Seal rings Leakage Embrittlement

Loss of oil,pollution,water ingress


Inadequate lubrication(idle period betweenuse),Contaminants (ingressof debris), thermaldegradation


Liner coating Leakage Wear Loss of oil,pollution,

water ingress

No direct system effectwhen leakage is not too

much, but repairs areneeded

Inadequate lubrication,Contaminants (ingress

of debris)galvaniccorrosion

Monitoring tank,saturation and

contaminationsensor

Liner bus Leakage Wear Loss of oil,pollution,water ingress


Inadequate lubrication,Contaminants (ingressof debris),galvanic corrosion


The guideline for the critical value for the saturation level of the oil as indicated for bearings will

kept as a guideline. For the oil level in the monitoring tank the critical value can be found in the

maintenance manual.

Gears

The failure modes and mechanisms of gears are summarized in Table 5. As can be observed the

local effect of gear failure for each component are vibrations of the thruster. The causes of

these failures are misalignment, insufficient / bad lubrication or shock loads. The cause and local

effects can be detected by the following parameters:

• Particles and water in oil

• Vibration near the gear

• Load


one of these parameters exceeds a critical value.



30

Table 5 Failure modes for the gears.

Assembly/co

mponent Failure mode Failure mechanisms

Local

Effect System effect Failure causes safeguards

Gear teeth Fatiguedamage teeth

Bending (loadconcentration on the toeof the gear)

Vibration ofthruster


Misalignment Inspection ofgear-set

Fatiguedamage teeth

Pitting (wear mechanismthat results from cycliccompressive loadstresses that exceed thematerial’s endurancelimit)



Insufficient/badlubrication

Inspection ofgear-set

Gear Tooth Toothbreakage/ tooth damage

Overload breakage Vibration ofthruster


Shock load Inspection ofgear-set

Toothbreakage/toot

h damage

Wear (moderate,abrasive, corrosive wear)



Insufficient/badlubrication

Inspection ofgear-set

Toothbreakage/tooth damage

Mounting error/bearingfailure/housingdeformation



Misalignment Inspection ofgear-set

Guidelines to determine the critical value are obtained from the Wärtsilä maintenance manual

and ISO standards for:

• Vibrations: RMS velocity

• Gear mesh frequency (GMF)

• Temperature

• Particles: contamination level

• Water: saturation level

This completes the identification of the failure modes for the critical components: bearings,

seals and gears. Knowing the failure causes and the contributing factors, the appropriate

condition monitoring parameters could be defined together with their critical values.

7.2.3 CMS development using RCM

Another way to decide whether CBM is a suitable maintenance strategy for a certain asset, is the

application of a Reliability Centred Maintenance (RCM) decision scheme as used by Wärtsilä

during the previous round of WCMC [6]. This scheme is based on subjective valuation of the

consequences of failure (how critical is failure ?), in combination with the failure rate. The

failure rate can either be constant in time (failure is bad luck, sometimes happens early,

sometimes late) or increasing in time (associated with some degradation process like wear). Thecriticality of failure follows from a FMECA analysis (see 7.2.2). Depending on the answers, the

RCM method advises either corrective or one of the types of preventive maintenance: on

condition, time based (calendar or use-based) or testing and inspection.

RCM on water filter separator

An RCM analysis was performed on one specific part (module) of the thruster system. In the

WCMC project [6] a special filter separator, which has been built in the lubrication system of the

thruster, was selected. This filter is used to take free water out of the lubrication oil. The filter

consists of the following submodels: pump, electrical pre-heater, filter separator, pressure

switch, water discharger, terminal box and automatic water drain. Every sub-module is



31

subdivided in components. For the E-motor of the pump the subdivision in components is

shown in Table 6.

Table 6 Breakdown of the E-motor of the pump.

For every failure mode the RCM decision scheme is used to fill in the RCM decision sheet. The

result for the E-motor of the filter set is shown in Table 7.

Table 7 RCM decision sheet for the E-motor of the pump.

The ball bearing of the E-motor is taken as an example here. For the ball bearing the RCM

decision is as follows:

• Failure is not revealed (or not apparent), it can be hardly seen or heard.

• The degradation is measurable (for instance by vibration measurement).

• Condition monitoring (vibration measurements) could be cost effective.

• Consequences of failure are not trivial (or not insignificant); costs related to damage

because of unfiltered oil in the system will influence the health of the thruster system.

The maintenance advice based on the analysis is that condition monitoring could be considered.

If condition monitoring is not cost-effective an optimal preventive maintenance (PM ) interval

can be applied.

7.2.4 Condition Based Maintenance

Once the condition monitoring system is defined, it must be applied to perform condition based

maintenance. This requires the translation of the collected condition data into appropriate



33

C t =1 (for T<183°C) Multiplying Factor for operating temperature

For the failure rate of a gear set a similar formula is used [14]:

λG =λG,B * C GS * C GP*C GA * C GL* C GT*C GV

λG = Failure rate of gear under specific operation, failures/million operating hours

λG,B = Base failure rate of gear as specified by manufacturer, failures/million operating hours

C GS =1+(Vo/Vd)0,7

= Multiplying factor considering speed deviation with respect to design

C GP =((Lo/LD)/0,5)4,69

Multiplying factor considering actual gear loading with respect to design

C GA =12,44*AE2,36

= Multiplying factor considering misalignment

C GL =(νo/νL)0,54

= Multiplying factor considering lubrication deviation w.r.t. design

C GT = 1 (t < 71 °C) = Multiplying factor considering the operating temperature

C GV = Multiplying factor considering the AGMA Service Factor



34

8 Validation of the guidelines

For the Wärtsilä pilot, the process followed during the CBM system development has been judged using the guidelines from section 6. This also validates the developed decision scheme.

8.1 Validation of the Wärtsilä pilot

The decision scheme has been used to assess the development process of the CM system on the

Wärtsilä thrusters. To limit the effort, only the bearing of the propeller gearbox in a steerable

thruster is used as the critical component in this case. The assessment is divided into two parts:

checking whether the asset is suitable for CBM and developing a customized CM system.

8.1.1 Is the asset suitable for CBM ?

A1 possible to identify critical components, failure mechanism & associated condition parameters ?

yes The FMECA and RCM analyses provided the following results:Critical components: For instance bearingsFailure mechanism: Surface fatigue, crack formation on rings, ballCondition parameter: Vibration, oil quality, oil contamination, wear elements

A2 can these parameters be measured ?

yes The sub questions regarding the measurements are answered as follows:

Sensors available ? Accelerometers, temperature sensors, contamination sensor andsaturation sensor

Location accessible ? Sensors inside during overhaul, sensors outside always accessibleData collection possible ? Wireless transmission, oil monitoring system

A3 can measured quantities be translated into maintenance intervals / failures ?

partly The options are:

Trending Thruster and operational parameters (PLC)Analyzing Vibrations and trends (visualisation, analysis)

Condition basedmaintenance andPrognostics (future)

Confidential

A4 does application of CBM yield f inancial or safety benefit ? yes The financial benefits of applying CBM are summarized as follows:

Financial benefits All thrusters must be available during Dynamic Positioning,when one fails the vessel will loose redundancy.In case of a warning or alarm one can stop the thruster:* Environmental pollution can be prevented.* Consequential loss can be prevented* Downtime shall be reduced, operational availability will be higher* In case of healthy thruster, vessel will not go into dock

A5 possible to implement CBM in organization ? yes The Wartsila service organisation is in place to support condition based maintenance of thrusters:

A6 is commercial CBM system available ? no In this case no suitable commercial CM system is available for the complete application, although

parts of the system are (vibration analysis, oil sensors).CBM Steerable Thruster Integrated commercial system for the specific thruster application is

not available.

Since all but the final questions have been answered positively, the asset appears to be suitable

for condition based maintenance, although there is some uncertainty about the translation of

the measured data into useful maintenance information. The plans for the prognostics are

described below. The final question has been answered with “No”, which means that the second



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part of the decision scheme is also used to decide how a customized CM system has to be

developed. This part is treated in the next section.

Planned prognostics for condition based maintenance of thruster bearings

At this moment a PLC system is available for collecting oil monitoring and operational

parameters, and a wireless transmission system coupled to a vibration monitoring system for

monitoring the thruster parameters. There is dedicated software for trending (visualization)

parameters and analyzing vibrations. The following method is used within Wärtsilä:

• Create baseline (thruster parameters: vibrations (reference spectrum), temperatures, rpm,

load….)

• Set alarm and warning values (based on guidelines: ISO4406, ISO/DIS10816-3, ISO 2372,

internal guidelines)

• Measurement of trends (absolute values, change of trends in time, vibration spectra)

o Trending/Analyzing software will be used to visualize trends

• In case of a warning an upcoming failure is under development:

o Trending/Analyzing software will be used to analyze parameters: vibrations (FFT,

enveloping, RMS), oil monitoring…

o The database of the applied bearings will be used in order to couple the failure

frequencies and harmonics to the bearings/accelerometers

o The software will be used to go back in time and see when the natural frequency of

the mounted bearing and the bearing casing started to develop.

• In case of growing wear the failure frequencies of the bearings and more harmonics will

come and sidebands will grow around the defect frequencies and around the natural

frequencies of the bearing. The bearing need to be replaced. The amplitude of the natural

frequency can be used to adjust the band alarm in the system.

• The system can be used to correlate the vibrations with other parameters.• The new online system will be upgraded with maintenance related info (replaced oil, filled

up header tank, etc…)

• Further development with regarding to prognostics are worked out.



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8.1.2 Development of the customized system

B1 is condition assessed directly or indirectly ? Both direct (condition) parameters and indirect (performance) parameters are measured:

Direct Vibrations by accelerometers near bearings and gear set.Oil properties / quality by saturation / temperature sensor (viscosity)and oil contamination by contamination sensor

3

Indirect Electrical power versus ship speedElectrical power versus propulsive force

B2 what is best method to measure ?

The subquestions regarding the measurements are answered as follows:

Suitable sensor ? Vibrations -> accelerometerOil quaility / contamination -> saturation / contamination sensor

Location accessible ? Accelerometers -> only during overhaul (outboard part)Saturation and contamination sensor -> In lubrication system(inboard)

Data collection possible ? Off-line -> localOn-line -> remote

Sample frequency ? Off-line -> periodical (e.g. once a week)On-line -> real-time \ daily

B3 how to obtain maintenance intervals from data ?

At present ideas about how the measured quantities can be translated into maintenance information

are worked out. The plans are summarized below and are described in more detail in [13]:

Prognostics Confidential

B4 determine critical values for measured parameters or trends

Critical values for the measured parameters are obtained from standards and other references:

Oil contamination ISO4406Vibration Severity ISO/DIS 10816-3, ISO2372 (ISO guide Machinery Vibration Severity)Vibration levels ABS Guide for Survey Based on Preventative Maintenance

Techniques (2003) / SNAME’s T&R Bulletin 3-42 “Guidelines for theuse of Vibration Monitoring for Preventive Maintenance”

B5 are there any boundary conditions to consider ?

The naval classification authorities have guidelines for application of CBM:

Det Norske Veritas (DNV) Guidance for Condition Monitoring, Classification Notes No.10.2,January 2003, Appendix H: Condition Monitoring for Propulsion andPosition Thrusters

American Bureau ofShipping (ABS)

Part 7 Section 14 Guidance for Survey Based on PreventiveMaintenance Techniques (2003), prescribing:* Extension of operation in case of CMS* Back-up capability* Items condition monitoring plan (rpm, load, data collection andanalyzing tools, location and orientation of sensors, samplingprocedure for oil analysis, schedule of data collection, baseline data,

etc.)* Annual report (machinery id, baseline, all data since opening, FFT,trend analysis, operational data (sea state, temperature, quarterlyoverall vibration data)* On-board documentation

Since all questions in the table have been answered, this shows that all required issues in the

development of a CM system have been considered for this pilot. Most of the issues could be

3”Advances in Real Time Oil Analysis”, David C. Schalcosky and Carl S. Byington, Penn State University

Applied Research Lab., 2000; “Using the Three Categories of Oil Analysis to Assist in Diagnosis”, Ashley

Mayer Noria Corporation. Source: www.oilanalysis.com



37

solved, but as was the case in the first part of the assessment (A3), the translation of the

monitoring data into maintenance information (issue B3) is not fully solved yet. Wärtsilä is

currently working on this aspect, which is the final key to make the CMS work. The details of this

work can be found in [13].

8.1.3 Conclusion Wärtsilä pilot

For the CMS of the thruster most of the questions were answered with yes, which means that

the asset is suitable for condition based maintenance. The only question that could not be

answered fully positively concerns the prognostics. It is especially that topic that is subject of the

present research activities at Wärtsilä and it is realized that this aspect will be decisive for the

success of the CMS.

8.2 Validation of the Gasunie pilot

Unfortunately, the Gasunie pilot could not be validated during this project using the developed

guidelines. However, the Gasunie pilot demonstrated that some specific additional aspects

should be considered during the development of a condition monitoring system:

do the applied sensors comply with safety regulations ?

are deviations from common maintenance practice (when implementing CBM ) authorized ?

8.3 Validation of the guidelines

The guidelines and the associated decision scheme (Figure 6-1) have now been applied to the

Wärtsilä pilot, which means that their usefulness can be judged. The conclusion is that that the

guideline gives a good structured method to asses whether an asset is suitable for CBM and, in

case of the unavailability of a commercial system, to determine how a CM system should be

developed. All relevant aspects in these selection processes are considered, which means that

ultimately a well motivated decision can be made to either apply or not apply condition basedmaintenance.



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9 ConclusionsIn this report a set of general guidelines for the application of Condition Based Maintenance

(CBM) are developed, resulting in a decision scheme that can be used by any asset owner

considering to use CBM. Moreover, additional information is provided about tools and methods

that can assist in making these decisions.

The pilot studies performed by Wärtsilä and Gasunie on a steerable thruster and a gas

compressor, respectively, are described and the development process of the CM system is

verified using the guidelines. This demonstrated that for the Wärtsilä pilot the large majority of

the relevant aspects have been considered. The only remaining issue is the prognostics part,

which must enable the translation of the monitored data into useful maintenance information.

During the analysis of the pilots, the presented decision scheme appeared to be a useful tool to

assess the process in a structured way. During the development of a customized condition

monitoring system, it also helps to ensure that no aspects are missed.



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cbm guidelines public

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