maxbe interoperable monitoring, diagnosis and …

MAXBE–DL–7.1–UCC–01–1.0October 2013

MAXBE INTEROPERABLE MONITORING, DIAGNOSISAND MAINTENANCE STRATEGIES FOR AXLE

BEARINGS

Deliverable 7.1: Specification Document for Condition-Based MaintenanceModel – Version 1

Document No.MAXBE–DL–7.1–UCC–01–1.0

October 2013

Prepared byUniversity College Cork

Contributing Partners:

• UCC

• UPORTO

• NEM

• IVE

• COMSA

• SKF


Abstract

The main objective of the project MAXBE is to provide validated and demon-strated concepts, strategies, and guidelines, for interoperable axle bearing moni-toring and diagnosis that will support railway operators and managers to deal withthe threats imposed by the existence of axle bearing defects.

Within the MAXBE project WP7 is concerned with developing optimization meth-ods for condition-based maintenance scheduling based on the predicted currentstate of assets, and in particular axle bearing condition.


Contents

1 Introduction 1

2 Background 3

2.1 Current Train Maintenance Scheduling . . . . . . . . . . . . . . . . . . . 3

2.2 Condition-based Maintenance . . . . . . . . . . . . . . . . . . . . . . . . 5

3 Problem Description 8

3.1 Maintenance Exams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Operational Trains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.4 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.5 Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

4 Input Requirements 12

4.1 Train Operating Company . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.2 Maintenance Depot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.3 Diagnostic Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.3.1 Axle bearing maintenance actions . . . . . . . . . . . . . . . . . 13

4.3.2 Maximum operational time/distance utilizing component degra-dation model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5 Constraints 14

5.1 Maintenance Exams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5.2 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.2.1 Renewable Resources . . . . . . . . . . . . . . . . . . . . . . . . 15

5.2.2 Consumable Resources . . . . . . . . . . . . . . . . . . . . . . . 16

6 Implementation 16

6.1 Optimizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

6.2 User Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7 Future Extensions 17

7.1 Maintenance Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7.2 Diagnostic Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7.3 User Interface Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18


1 Introduction

The objective of the MAXBE Project is “. . . to provide validated and demonstratedconcepts, strategies and guidelines for the interoperable axle bearing monitoring anddiagnosis that support the railway operators and managers dealing with the threatsimposed by the existence of axle bearing defects”. More specifically in WP7 (Develop-ment of Asset Management Technologies), the objective, as stated in the Descriptionof Work, is to develop alternative asset management models along with associatedsoftware tools for planning condition-based maintenance for axle bearings and wheels.

This document provides a specification of work that is to be undertaken for the firstiterative cycle of software development, with particular emphasis on optimized main-tenance scheduling. Subsequent deliverables will place greater emphasis on assetcondition diagnostics, end-user interface development, and system integration.

Condition-based maintenance of axle bearings and rolling stock components can bringlarge benefits to both the fleet maintainer and fleet operator in terms of fleet availability,reliability, safety, and in the reduction of overall maintenance costs. However, this canonly be achieved effectively if:

• The maintainer can easily visualize and understand the condition of all of theirassets and have a high level of confidence in diagnostic alerts.

• Maintenance actions and deadlines for completing the actions are recommendedto the maintainer to provide an important input into decision-making based on awealth of asset knowledge and information.

• With the introduction of condition-based maintenance the periodic maintenanceexams which are not eliminated (such as oil sampling, bearing reconditioning,bearing replacement, etc.) have maintenance intervals that are optimized for theasset and its operating and environmental conditions.

• The shift in culture and business practice from traditional periodic maintenanceto on-condition maintenance is fully embraced by management personnel, fleetengineers and technical staff. This requires that diagnostics and planning tools(hardware and/or software) not only perform their function reliably, but are alsointuitive and straightforward to use. An interface of poor usability and low accep-tance will result in the technology and cultural change being rejected.

Work packages WP2-WP6 are focussed on the development of wayside and onboardsensor and data acquisition systems for the detection of axle bearing faults. Suchsystems generate information about the current condition of train assets, which can beprovided directly to the train maintainer and/or operator.

However, information itself is worthless unless decisions are taken as to what mainte-nance interventions are to be performed, their priority, and exactly when they should beundertaken. WP7 addresses the need for completing links in the stages of the overallprocess of condition-based monitoring by channelling work in the following areas:

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• Development of a tool for the optimization of maintenance exam schedul-ing given the current scheduling of planned exams and the need for dynamicintroduction of condition-based (preventive and corrective) maintenance exams.

• An integrated diagnostics and asset condition assessment combining infor-mation from different condition monitoring sources (wayside/on-board, acoustic,vibration, temperature).

• Development of interfaces and web technologies for the sharing, presenta-tion and feedback of information and integration with current computerized main-tenance management systems (CMMS).

The scheduling of maintenance tasks is vitally important to the success or failure of acondition-based maintenance approach and its implementation. A rolling stock main-tainer for either freight or passenger stock will have more information about the con-dition of their assets thanks to technological advances in wayside and on-board mon-itoring systems and developments within the MAXBE project. Together with the workdescribed above, the maintainer will be able to determine the maintenance tasks to beperformed and the time window available to perform them. This time window could beas short as a few days (or even hours) or longer than 3 months, depending on the as-set condition, its expected rate of future deterioration, and the consequences of failure(safety, economic, customer service provision, etc.).

A maintainer with a large number of trains in a fleet will struggle to manually sched-ule condition-based maintenance exams in an efficient manner, especially as conditionalerts will potentially be generated on a day-to-day basis. The resulting maintenanceexams will have to be slotted into available time windows, or if unavailable will requirethe re-planning of the current planned maintenance schedule. The maintainer wouldthen need to take multiple factors into consideration in order to optimize the organiza-tion’s maintenance and business performance, some of which include:

• Train units already scheduled for maintenance;

• Grouping of preventive and corrective maintenance exams;

• Allocation of staff resources, e.g. mechanical/electrical technicians, engineers,etc.;

• Allocation of depot facilities, e.g. pit road, wheel lathe, roof access, bogie drop,cleaning, etc.;

• Asset life cycle costs;

• Train immobilization costs.

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2 Background

2.1 Current Train Maintenance Scheduling

Maintenance of train fleets has traditionally been carried out on the basis of cyclicmaintenance interventions/exams, which are commonly undertaken according to themanufacturer maintenance manual. Maintenance manuals provided by rolling stocksuppliers establish which inspections and/or repair activities must be performed withineach maintenance intervention, which are time-based or distance-based actions.

This periodical planning based on time and/or distance is the most extended approachemployed in fleet maintenance. The adoption of this maintenance strategy derivesprimarily from the following:

• In opposition to what is done in rail infrastructure maintenance where crews movealong the line to inspect the assets, maintenance tasks and inspections for rollingstock are almost always performed in the workshop (there is little defect iden-tification of the rolling stock outside the depot, except those faults/incidents thedriver can identify during circulation). Therefore, given that there is no acquisitionof rolling stock assets condition until it is inspected at the depot, time/distance-based inspections must be fixed.

• Undertaking fleet maintenance inevitably provokes disruption of train operations,which can be significantly high when there is a need for carrying out importantmaintenance tasks (such as major engine overhaul). To minimize the fleet main-tenance impact to operations, maintenance tasks are joined as much as possibleso as to minimize the number of occasions that the trains must be dispatched tothe workshop.

• Passenger and personal safety is critical to the railway industry and maintainersmust do their utmost to ensure the safety of their train fleets. Traditionally this isachieved through regular and consistent safety checks and periodic inspectionsand replacement of components.

An example of a preventive maintenance schedule based on this strategy is shown inand Figure 2.1 below, with the exam attributes given in Table 2.1. There are four differ-ent maintenance interventions/exams (A,B,C,D) that are held periodically for each trainunit (RS-01, RS-02, RS3, etc.). Note that the duration and the resources consumed ineach maintenance exam increase with time/distance.

On the other hand, preventive maintenance planning has to be combined with correc-tive maintenance. In spite of the periodical inspections at the workshop, failure of cer-tain components will require corrective maintenance exams to be undertaken promptlyand without delay. The affected train must be brought to the depot earlier than originallyplanned.

Because of not only the possibility of failure occurring during operation, but due to logis-tic or operational constraints (train location in relation to the workshop, traffic demands,shunting operations, etc.), the preventive planned maintenance has to accommodate

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Exam Interval (Wks) Duration (Hrs) StaffA 4 10 3B 12 20 3C 24 40 4D 96 80 4

Table 2.1: Sample Maintenance Exams.

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26RS-‐01 A A A B A D D A A B A CRS-‐02 A A A B A A A B ARS-‐03 C A A A B A A A BRS-‐04 A A B A C A A B A ARS-‐05 B A A A B A C A A BRS-‐06 A B A A A B A C A A

(a)

0

50

100

150

200

250

300

350

400

450

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Week

Staff

Hou

rs

Trains in Dep

ot

Depot Resource Usage Trains in Depot Staff hours

(b)

Figure 2.1: Sample (a) Planned Preventive Maintenance Schedule and (b) ResourceUsage for Maintenance Depot.

changes in the schedule. Maintenance plans determine upper limits on time and/ordistance by which periodic maintenance exams must be undertaken. Maintenanceplanners must respect these limits, taking into consideration that urgent corrective ex-ams will have priority over planned routine exams, and therefore delay them to a laterdate.

Some research related to the suitability of the current approach has been undertakenas the maintenance philosophy of other industries are progressively moving from pre-ventive periodic maintenance to condition-based maintenance in the belief that this willlead to a reduction of overall costs. Given that maintenance planning represents akey factor in an industry where machinery (train fleet) can be exceptionally costly, theadoption of new strategies that will enable an optimization of rolling stock maintenancecosts seems unavoidable.

One potential source of optimization of the current maintenance strategy may arisefrom the grouping of maintenance tasks to minimize train dispatching to the workshop,resulting in a higher maintenance/replacement of components than required (manycomponents are maintained/replaced much before its lifespan has been consumed).Notwithstanding, as will be discussed later, rolling stock maintenance has a long list of

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particularities/constraints that may limit the potential benefits of condition-based main-tenance.

Axle bearings are one of the most important assets in a railway vehicle. Due to theimpact that axle bearings have in safety, and in operation and maintenance costs,related to vehicle downtime, and vehicle and infrastructure damage, railways cannotafford to operate without a strategic plan for axle bearings. Predictive maintenanceor condition-based maintenance is the approach which is being applied and used tomaintain axle bearings. The manner to maintain them cannot be corrective, due to thereasons expressed above, nor preventive (time or travelled distance based) due to thehigh cost of these assets. Thus the approach should be a maintenance strategy whereparameters, which can give information/indication about bearing condition, should bemeasured.

Bearing maintenance is performed in two ways:

1. Temperature and lubricant (grease) analysis in fixed intervals of travelled distancerequire the following maintenance actions:

• Gaps verifications;

• Verifications of the lubricant state;

• Verifications of the losses of lubricant or mass;

• Verifications of the bearings body.

If in one of these tasks an anomaly is detected, generally the entire wheelsetis withdrawn from service and is replaced with a spare one. Thereafter, thewheelset is sent to the repair workshop and is analyzed by specialists. As thebearing is not removed from the vehicle to perform the analysis we call this an“on-line” analysis. This analysis is performed in light maintenance shops andduring the intervals the bearing condition is not known.

2. In interventions like wheel changing or during bogie overhauling, the bearings areremoved from the wheelsets. Whenever this occurs the bearing is dismantled andanalyzed. If a defect is found the bearing is rejected and replaced by a new one.The analysis is mainly based in visual inspections. This is more like an “off-line”analysis and is performed in heavy workshops.

2.2 Condition-based Maintenance

A paradigm in the management policy of rail systems has recently emerged that booststhe development of new methodologies for monitoring the condition of the rail infras-tructure and rolling stock in order to gradually substitute the merely preventive mainte-nance by maintenance methodologies based on the actual condition of railway vehiclesand tracks. Condition-based maintenance (CBM) is the strategy by which maintenanceactions are undertaken when the component of the system reaches a specific conditionor state, a forerunner of in-service failure. Often, this implies that components are notreplaced early before the end of their useful life and remain in service until a symptom

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of degradation is detected or a measured parameter of the component exceeds toler-ance limits. This can result in improved reliability and availability, due to the avoidanceof unexpected in-service failures, and lower life cycle costs. For CBM to be successful,there are some issues to be considered:

(i) the accuracy of the inspection;

(ii) the inspection interval, that has an associated cost;

(iii) the definition of the most representative key performance indicators

(iv) the condition limit.

A literature review on condition-based maintenance is now presented for a better con-textualization of the problem. An overview of applications of maintenance optimizationmodels is presented by Dekker [1]. Christer and Wang [2] analyze the problem of con-dition monitoring of the wear of a component by a model which minimizes the expectedcost per unit time over the time interval between two inspection times. Barbera etal. [3] discuss a condition-based maintenance model defined by dynamic programmingwhich considers exponential failures and fixed inspection intervals for a two-unit sys-tem in series. The probability of failure is exponential and the failure rate depends onthe condition of each unit, which is monitored at equidistant time intervals. The authorsalso consider that each unit can fail only once within an inspection interval and whenone or both units fail, the whole system fails. After a maintenance action is performedthe monitored condition indicator variable takes on its initial value-perfect maintenance.

Regarding railway systems, specifically maintenance in railway tracks, Higgins et al. [4]develop a model to help solve the conflicts between the train operations and the schedul-ing of maintenance activities in railways and its formulation is based on the integerprogramming. The model is applied to a 89 km track corridor on the eastern coast ofAustralia considering a four day planning horizon.

Budai et al. [5] present a preventive maintenance model where a schedule for the main-tenance activities has to be found for one link by minimizing the sum of the possessioncosts and the maintenance costs. The authors focus on the medium-term planning,determining which preventive maintenance work will be performed in what time peri-ods (month/week/hours). More recently, Vale et al. [6] propose a model designed tooptimize the tamping operations in ballasted tracks as preventive maintenance. Globaloptimization (the model is formulated as mixed 0-1 linear program) is used to predictand to schedule tamping taking into account the evolution of track degradation overtime; the track layout; the dependency of the track quality recovery on the track qual-ity at the moment of the maintenance exam; and the track quality limits that dependon the maximum permissible train speed. The model is applied to two railway trackstretches of the Portuguese Northern Railway Line, the railway line to be considered inthe MAXBE project.

Regarding maintenance of railway vehicles, Ma [7] proposes a CBM model for themaintenance of freight car components based on the life cycle cost. Hani et al. [8]present a simulation and optimization methodology for a train maintenance facility by

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choosing the best scheduling policy. The authors use multi-objective optimization tech-niques based on genetic algorithms coupled with simulations. Bohlin et al. [9] combinecondition monitoring with online maintenance planning by using an adaptive planningsoftware module to find suitable vehicle movement plans, and a heuristic packing mod-ule to rebuild maintenance planning. More recently, Doganay and Bohlin [10] presenta mixed integer programming model for fleet maintenance schedule optimization.In Figure 2.2 the relation between maintenance policies and life cycle cost (LCC) ispresented.

Figure 2.2: Maintenance policy and LCC [7]

Despite, as described above, the fact that there has recently been some researchcarried out on applying CBM to optimize train fleet maintenance, it has to be saidthat CBM is far from being vastly employed in train fleet management. This is mainlybecause there are a large number of constraints in the application of CBM in rollingstock maintenance, which makes much more complex its implementation than in otherfields such as railway infrastructure maintenance, where the CBM strategy has beenadopted by many European Infrastructure Managers.In comparison to railway infrastructure maintenance, the implementation of CBM phi-losophy in train fleet management has the following added difficulties:

(a) Train fleet is composed of moving assets whose routes and hence, environmentalfactors (weather conditions, track quality, etc.) affecting their condition may vary

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daily. Because of this, the evolution of rolling stock components’ condition is moredifficult to predict and monitor than a fixed asset of railway infrastructure.

(b) Different rolling stock, or even the same type of rolling stock with different age orusage, requires different maintenance scheduling. As an example, the list of main-tenance exams (number of inspections, especially the replacement/refurbishmentof components) increases considerably with age/km covered. Therefore, in somecases an “individualization” of the maintenance planning is required, which meansthat specific maintenance scheduling has to be defined for each train.

(c) The number of components whose condition is monitored systematically is muchmore reduced in rolling stock maintenance than those monitored in railway infras-tructure. This implies that the weight of predictive maintenance (cyclic inspections)over the whole maintenance works is higher in train fleet maintenance than in rail-way infrastructure.

(d) Trains (especially high-specification passenger trains) are complex assets and aremade up of hundreds of individual parts. Maintenance on components are typicallygrouped together, to allow numerous maintenance tasks to be performed consec-utively. Introducing CBM for some of these parts, will inevitably make maintenanceplanning more complex.

(e) Rolling stock maintenance is undertaken solely at the depot, which implies thatthe train has to be put out of service for some time (maintenance time + transfertimes), whereas railway track is maintained during night shifts and reopened justbefore the first service.

(f) How the maintenance of rolling stock has to be done is specified in a maintenancemanual elaborated by the manufacturer. If it is not followed accordingly, it mayresult in a liability issue.

According to this, the implementation of CBM in train fleet maintenance has to accountfor certain constraints (immobilization costs, need of a high level of predictive main-tenance, moving assets going through different environments, etc.) that may limit thepotential benefits of a CBM approach.

3 Problem Description

The online maintenance scheduling problem involves scheduling the maintenance of aset of trains over a fixed horizon H, subject to constraints involving the capacity of themaintenance depot and the requirements of each maintenance exam, such that a costobjective is minimized. This objective function may take into account the monetary costof performing the maintenance as well as additional costs such as train immobilizationcosts, rolling stock costs, etc.

Train immobilization costs can be decisive for a maintenance decision, because thetrain operating companies (TOC) have to use reserve trains for keeping up the service.

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Figure 3.1: Two Baselines Approach

These reserves also lead to high costs for being kept prepared as a backup. Further-more, additional trains may require maintenance in an online manner over the horizon,requiring re-optimization of the maintenance schedule. The model therefore consistsof two major baselines. One involves the planned or preventive maintenance exams,the other involves the unplanned CBM (see Figure 3.1).When optimizing and re-scheduling the maintenance plan, it is also important to provethe feasibility of the solutions for the adjacent timetable dependencies. Because thevehicles use a predetermined train allocation plan, the maintenance decisions need tofit to the timetable. The verification of the feasibility of the maintenance optimizationsolutions from the timetable perspective will be considered in a further step throughthe analysis of different scenarios. This will allow a proven practical certificate for theresults.

3.1 Maintenance Exams

A maintenance exam Oi consists of a set of tasks to be performed. It has an associ-ated train type OT

i , where the train types can be differentiated between multiple units,single locomotives or wagons for passenger and freight traffic. From the models per-spective these all are assets that require maintenance. The respective assets differ,per train type, in their maintenance exam intervals, required capacities and adjacentcosts. This means that the developed model will be able to use the same methodologyfor optimizing the maintenance schedule for any kind of vehicle (see Figure 3.2).Each task, aij has an associated duration, durij, and a set of resource requirements,Rj. For example, x workers and machines w, z, are required for a duration of y minutesto remove the carriage from the bogie. We will initially consider all tasks to be non-preemptive, i.e. once an task starts it must run to completion. In a further stage of the

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Figure 3.2: Model can handle different train types

model development the potential for using variables to consider delays in maintenancetask processing can be examined.

The resource requirements differ depending on the type of resource (described in thefollowing section). There may be a predefined ordering on subsets of tasks, where atask aij must be processed before task aik. These dependencies have to be researchedand defined prior to the optimization.

3.2 Resources

We consider two main resource types: renewable resources and consumable resources.A renewable resource is one which returns to its previous state once finished process-ing a task (e.g. a machine/worker), a consumable resource decreases with use bytasks and can only be replenished through addition of new stock (e.g. axle bearings).

3.3 Operational Trains

There is a fixed set of train types belonging to every TOC, for each type there is aminimum number that should be operational at all times, because of the high expensesfor immobility of trains. This number depends on the timetable, the vehicle allocationplan of the TOC and the number of reserve trains. A reduction below this value willresult in a large penalty in the immobilization cost based on estimated cost impactof failure of the TOC to meet its scheduled operation. There will also be a (smaller)penalty for reducing the number of reserve trains below their minimum preferred level.

3.4 Objective Function

Our objective function for minimization may involve the following costs:

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• Train immobilization costs, which are dependent on the TOC operational require-ments in the immobilization period:

– Number of trains of same type required in each time period.

– Preferred reserve number of trains of same type.

• Life-cycle costs including maintenance.

3.5 Outputs

The outputs from the optimization algorithm are as follows:

• Optimal schedule for maintenance depot

– Long term Assignment of maintenance exams to days

– Short term Start times for all tasks in maintenance exams

• Train availability for the TOC: Train type and identifier, latest arrival at depot, ear-liest departure from depot.

• Updated maintenance information for CMMS: Train identifier, component identi-fier, date, and maintenance action performed

Figure 3.3 provides an overview depicting the inputs and outputs for the online mainte-nance scheduling problem.

Optimization

CapacityResource Attributes

StockCurrent Schedule

Depot

Operational ReqsReserve Reqs

Costs

TOC

Train/Component IDMaintenance Action

Diagnostics

Reoptimized Schedule

Depot

Train ImmobilityTOC

Train/Component IDMaintenance Actions

CMMS

Figure 3.3: Inputs and Outputs for Online Train Maintenance Optimization

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4 Input Requirements

The input requirements for the maintenance scheduling problem come from three mainsources: the train operating company, the maintenance depot, and the diagnostic tools.The TOC inputs the type and number of trains to be maintained, the maintenance depotoffers its capacities (workers, machines, stock) to maintain the trains.

4.1 Train Operating Company

• Service Demand : A train has an associated type. For each type there is a mini-mum number of operational trains which should be available at all times to meetthe train schedule, and a preferred number of reserve trains. There is also animmobilization cost associated with each train type (which may be dependent onthe number of currently available trains of that type).

As mentioned before (Section 3) the availability of their trains is very importantfor the TOC. Every hour in which the trains are not in service causes costs. Thecosts depend on the number of reserve trains required due to the frequency andduration of maintenance intervals.

• Maintenance Exams: A maintenance exam, such as axle bearing repair / replace-ment, consists of a number of tasks and a set of relationships between subsets oftasks. Each task has an associated duration and a set of resource requirements.

4.2 Maintenance Depot

• Depot Capacity : The maintenance depot has a fixed capacity stating the maxi-mum number of trains that can be processed concurrently. More specifically, adepot has a set of R “roads” of fixed length where maintenance can be performed.These roads may have associated resource attributes, e.g. a pit.

• Resources:

– Machines: The number of machines of each type in the depot, the capacityof each machine (i.e. the number of tasks it can concurrently handle).

– Workers: A set of worker typesW; the maximum number of workers of eachtype available at any time (Wmax

i for workers of type i); and a time dependentcost of a worker (e.g. day-shift versus night shift cost). The type of worker isan attribute which states which tasks the worker can perform.

• Current Maintenance Schedule: Set of current maintenance exams, task starttimes and associated resource usage. The current maintenance exams mainlyinclude planned maintenance tasks.

• Rolling stock: The current stock level of each stock type available, and futurestock arrival times and quantities.

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Figure 4.1 illustrates attributes of a sample exam and a sample resource. The sampleexam is to be performed whichever condition of 75,000km or 26 weeks occurs first, andhas only one task of duration 4 hours requiring 2 mechanical technicians and a shorepower supply. The sample depot road has capacity, power and resource attributes.

TaskTask Title B ExamTask Description (description)

Maintenance IntervalMax. Mileage (km) 75000

or Max. Period (weeks) 26or Max. Operation time (hrs) -‐

Task Duration (estimated)Task Duration (hrs) 4

Staff ResourcesMechanical Technician 2Electrical Technician 0Either Mechanical or Electrical 0

Depot RequirementsRoad (ground level) 1

or Road with Pit 0Shore power supply 1Overhead power supply 0Bogie drop pit 0Underfloor wheel lathe 0

(a)

Depot RoadRoad Number 4

Road Description (text description)Capacity

Capacitiy (num. of trains) 2or End Open(1) or Closed(0) 1

PowerShore supply 1Catenary 1

ResourcesRoof access (gantry) 1Road with Pit 0Bogie drop pit 0Underfloor lathe 0Train jacking equipment 0

(b)

Figure 4.1: Attributes for sample exam and sample resource.

4.3 Diagnostic Tools

4.3.1 Axle bearing maintenance actions

In addition to the periodic maintenance that has been described in Section 2.1, severaltasks with systematic character are held in the repairing workshops at the half-life of thewheel sets potential, based on the the bearing diagnostics. Maintenance actions suchas the verification of the noise/sound, vibration, temperature, lubricant contaminationor loss are carried out before the wheelset removal operation.

After performing these analyses, the shaft bearing is dismantled and washed, allow-ing the complete inspection of the axle bearing (visual inspection, disassembly of thebearing, full examination of the components). If in one of these exams any defect orproblem is detected, the bearing or the entire wheelset (if it is more appropriate) isreplaced by a new one.

4.3.2 Maximum operational time/distance utilizing component degradation model

The wayside and on-board monitoring systems under development in WP3 and WP4as well as their integration in WP5 are multi-parameter condition monitoring systemsbecause they use vibration, temperature and acoustic techniques for monitoring thecondition of axle bearings.

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Figure 4.2: Evolution of bearing life [11].

The advantage of simultaneously employing different monitoring systems is that faultsin axle bearings may be detected at an early stage of development, before they becomeproblematic as shown in Figures 4.2 and 4.3.

The maximum duration until the train becomes inoperational (i.e. the maximum timebefore maintenance must be performed) depends on: the monitoring technique used toevaluate the condition of the bearing; and the condition of the bearing when it is mon-itored or subjected to inspection. It is expected that more detail will become availableon the maximum time for performing maintenance during the project development.

5 Constraints

5.1 Maintenance Exams

Let sij be the start time assigned to task j of exam i. The start time of all tasks isbounded by the earliest and latest possible start times of their associated exam.

esti ≤ sij ≤ lsti ∀ aij ∈ Oi, ∀ Oi ∈ O (5.1)

For each exam Oi there is a set of tuples Preci stating the ordering on certain pairs oftasks in Oi.

sij + durij ≤ sik ∀ (aij, aik) ∈ Preci (5.2)

Additionally there may be constraints enforcing a minimum/maximum time allowed be-tween the completion of a task and the start time of its ordered pair task in Preci. Let

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Figure 4.3: Vibration analysis [11].

MaxLagi (respectively MinLagi) be the set of tuples from Preci having a maximum(minimum resp.) time lag constraint.

sij + durij + lagminij ≤ sik ∀ (aij, aik) ∈MinLagi (5.3)

sij + durij + lagmaxij ≥ sik ∀ (aij, aik) ∈MaxLagi (5.4)

Observe that constraints of type (3) subsume their associated precedence constraint(2).

5.2 Resources

5.2.1 Renewable Resources

The consumption of a resource Rj (be it machine or worker type) must be less thanthe maximum allowed (Rmax

j ) at all times. Let Arj be the set of tasks requiring resource

j, xijt be a (0,1) variable indicating task i uses resource j in time period t, and rijbe the resource consumption required to perform task i. (We assume the resourceconsumption to be constant across the duration of the task, in later iterations this maybe changed.) ∑

i∈Arj

(xit ∗ rij) ≤ Rmaxj ∀ Rj ∈ R, ∀ t ∈ H (5.5)

Additionally there may be constraints enforcing that certain subsets of resources maynot be used concurrently. For each resource Rj there is a set of incompatible resources

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IncResj. The non-overlapping constraint enforces that no resource in IncResj canoverlap in operational mode with Rj. Let yjt be a (0,1) variable indicating resource j isused in time period t:

(yjt +∑

i∈IncResj

yit) ≤ 1 ∀ Rj ∈ R, ∀ t ∈ H (5.6)

5.2.2 Consumable Resources

For each stock type j, the stock level Stockjt at time t must be non-negative and equalto the stock level in the previous time period plus the new stock introduced (Snew

j,t ) minusthe stock consumed by the tasks which started in time period (t− 1). Let S be the setof stock types, As

j be the set of tasks requiring stock j, and xit be a (0,1) variableindicating task i starts in time period t:

Stockjt ≥ 0 ∀ j ∈ S, ∀ t ∈ H (5.7)

Stockjt = Stockj(t−1) + Stocknewjt −

∑i∈As

j

xi(t−1) ∗ rij ∀ j ∈ S, ∀ t ∈ H (5.8)

6 Implementation

As specified in [12], the online maintenance system and associated user interfaces willbe developed using open-source tools. Figure 6.1 illustrates the interaction amongstthe different components. A diagnostic tool initiates the process by requesting a main-tenance action. Inputs are then requested from the maintenance depot and the TOC.The function CombineAction assesses whether the maintenance exam requested canbe combined with a planned maintenance exam on the same train.

The set of exam requests, the existing schedule and the operational requirements ofthe TOC are then passed to the Optimizer. This returns the optimal schedule, which issent in different forms to the maintenance depot, the TOC, and the asset managementsystem through the Update* functions.

6.1 Optimizer

We envisage that the optimizer will be implemented in Choco [13] which is a Java libraryfor constraint programming, containing state-of-the-art algorithms and techniques forsolving combinatorial problems with a number of dedicated techniques for the schedul-ing domain. This software is open source, distributed under a BSD license. A numberof applications (e.g. cloud automation software) utilize Choco for embedded optimiza-tion components.

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Middleware Optimizer Depot Diagnostics TOC CMMS

MaintenanceAction(ID, action)

getCurrentM()Maintenance Input

getCurrentO()Operational Input

Operational InputCombineAction()

ExamOptimize()

ScheduleUpdateM(Schedule)

UpdateO(Schedule)UpdateA(Schedule)

Figure 6.1: UML sequence diagram.

6.2 User Interfaces

The optimizer will require inputs from a number of sources, including monitoring sys-tems and maintenance planning and management tools. Work will be undertaken dur-ing the project to identify requirements and suitable interfaces for data and informationtransfer. This may require the need for middleware to act between the optimizer andthe third-party systems.

The work will investigate the most suitable approach from the end-user’s perspectiveconsidering ease-of-access and security. Output information will be generated so as tobe compatible, where possible, with existing asset management systems. In order tominimize traffic between components we envisage that the configuration inputs (capac-ity and fixed resources of maintenance depot, preferred reserve level for train types ofTOC, etc.) will be stored at the middleware level, however this needs to be discussedwith the consortium end users.

7 Future Extensions

7.1 Maintenance Scheduling

Future iterations of model development will include the following variations on the cur-rent model:

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• maintenance optimization for many TOCs and one maintenance depot;

• maintenance optimization for many TOCs and many maintenance depots.

Furthermore, a richer set of diagnoses and maintenance actions will be considered.

7.2 Diagnostic Techniques

Historical data will be used, as appropriate, to understand the evolution of defects. Asthe capability of the diagnostics systems in WP3, WP4 and WP5 are enhanced, we willintegrate this sophistication into subsequent versions of our maintenance technology.Furthermore with greater levels of sophistication and scale of diagnostic capability itwill become a great opportunity to cross-reference alarm states to improve the qualityof information available to the condition-aware maintenance system.

In order to optimize the type and frequency of maintenance tasks carried out on aparticular asset such as axle bearings, it is important that condition-based monitoringsystems provide accurate and reliable information on the current condition of assets.Incorrect or incomplete information could lead to the wrong actions being planned, orworse case, not being performed at all.

In parallel to the development of a maintenance scheduling tool, work will be under-taken to bridge the gap between the outputs of WP3, WP4 and WP5 and the inputsinto WP7.

It is envisaged that this will include the development of advanced diagnostics that willmodel the behaviour of assets such as axle bearings and predict their monitored output(e.g. temperature or vibration levels) given their current and historic operating condi-tions. Model construction will focus on using real empirical data, rather than theoreticalmodels. Work will also be focused on the integration and analysis of data from differ-ent monitoring sources and information from CMMS systems, following on from workachieved in WP5. It is hoped that not only can diagnostic alerts be integrated andassessed collectively, but information from CMMS systems can be used to provideadditional confidence in asset condition assessment.

7.3 User Interface Design

An important aspect to the development of software tools and applications is to designthem such that they are quick, easily accessible, intuitive and attractive to use. Workwill be done in WP7 to develop web-technologies for online applications and to createuser interfaces that increase the likelihood of end-user product acceptability. Consid-eration will be given to the communication of information and alerts through devicessuch as PCs, tablets and mobile phones.

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References

[1] Rommert Dekker. Applications of maintenance optimization models: a review andanalysis. Reliability Engineering & System Safety, 51(3):229–240, 1996.

[2] A. H. Christer and W. Wang. A simple condition monitoring model for a directmonitoring process. European Journal of Operational Research, 82(2):258–269,1995.

[3] Fran Barbera, Helmut Schneider, and Ed Watson. A condition based maintenancemodel for a two-unit series system. European Journal of Operational Research,116(2):281–290, 1999.

[4] Andrew Higgins, Luis Ferreira, and Maree Lake. Scheduling rail track maintenanceto minimise overall delays. In The 14th International Symposium on Transportationand Traffic Theory, Jerusalem, Israel, 1999.

[5] Gabriella Budai, Rommert Dekker, and Dennis Huisman. Scheduling preventiverailway maintenance activities. In SMC (5), pages 4171–4176. IEEE, 2004.

[6] Cecılia Vale, Isabel M Ribeiro, and Rui Calcada. Integer programming to optimizetamping in railway tracks as preventive maintenance. Journal of TransportationEngineering, 138(1):123–131, 2011.

[7] Qian Ma. Condition-based maintenance applied to rail freight car components:The case of rail car trucks. Master’s thesis, Massachusetts Institute of Technology,Department of Civil and Environmental Engineering, 1997.

[8] Yasmina Hani, Lionel Amodeo, Farouk Yalaoui, and Haoxun Chen. Simulationbased optimization of a train maintenance facility. Journal of Intelligent Manufac-turing, 19(3):293–300, 2008.

[9] Markus Bohlin, Malin Forsgren, Anders Hoist, Bjorn Levin, Martin Aronsson, andRebecca Steinert. Reducing vehicle maintenance using condition monitoringand dynamic planning. In 4th IET Intl. Conf. on Railway Condition Monitoring(RCM’08), June 2008. IET, 2008.

[10] Kivanc Doganay and Markus Bohlin. Maintenance plan optimization for a trainfleet. Computers in Railways XII: Computer System Design and Operation in theRailway and Other Transit Systems, 114:349, 2010.

[11] SKF. SKF bearing maintenance handbook, 2010.

[12] MAXBE Consortium. Description of Work.

[13] Choco. Java Library for Constraint Programming. http://www.emn.fr/z-info/choco-solver.

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http://www.emn.fr/z-info/choco-solver

http://www.emn.fr/z-info/choco-solver

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