cbm decision making

CBM Decision making

P-F IntervalOptimized CBM decisions

History, Anatomy, Nature of data

2OMDECOptimal Maintenance Decisions Inc.Optimal Maintenance Decisions Inc.

Two Bearings

1/3.5

1/7

Warning 2 daysOK

Failed

P-F = 2 Days

Noisestarts

Inspection interval 1 week

Insp. interval 1 day

1. The lower the Mean Time Between Failure (MTBF), the more frequently you monitor?

2. The more critical, the more frequently you monitor?

Assertions:

Very critical

Not so critical

(MTBF = 3.5 years)

(MTBF = 7 years)

Brg A

Brg B

Two BearingsRisk

Co n

dit i

onal

Pro

babi

l it y

of

Fai

lure

Noisestarts

OK

Warning 2 wksFailed

P-F = 2 Weeks

Functionalperformance


Initial inspection intervalIs CBM for the failure modein question applicable? (Isthere a clearly identifiablecondition indicator? Is thewarning time adequate?)

Is CBM for the failure modein question effective? (Is

there an economical CBMtask and interval that will

avoid or reduce, to atolerable level, the

consequences of thefailure?)

NO

YES

is the warning period of theorder of days, weeks, or

months?

Days (Weeks, Months)

How many days (weeks,months)?

Initial inspection Interval =X/2

X Days (Weeks, Months)

CBM not applicable or noteffective -> Descend to next

task type in the RCMalgorithm.

NO

YES


*

The Elusive P-F Interval

Co

nd

itio

n

Working Age

Potential Failure P

P-FInterval

Warning Interval

Failure F

InspectionInterval

Inspection data

*

Ideal

?

?

?

Real


The conventional CBM decision method from Nowlan & Heap, (Moubray)

Co

nd

itio

n

Working age

P-F Interval

Detectable indication of a failing process

Detection of the potential failure

CBM inspection interval:

Potential failure, P

< P-F Interval

Net P-F Interval

Functional failure, F


The P-F Interval method

Assumes that:

1. The potential failure set point, P, of an identifiable condition is known, and that

2. The P-F interval can be found and is reasonably consistent (or its range of variation can be estimated), and that

3. It is practical to monitor the item at intervals shorter than the P-F interval


Obstacles to the practical application of the P-F decision model

1. One may mistakenly infer from the P-F graph a single condition indicator influences failure probability.

2. P and P-F can be random variables.

3. P may not be constant for different working ages of the item.

http://www.omdec.com/wiki/tiki-index.php?page=Lexicon#randomvariable

Moubray (RCM II) addresses two extreme cases

F2 F3 F1

0 2 3 4 51

Failures occur on a random basis

Inspections at 2 month intervals

PF detected at least 2 months before FF.

PFFF

Age (years)

Special case 1 – completely random (age independent, dependent only on condition monitoring data)

0 20 30 40 5010Operating Age(x 1000 km)

PF

FFTre

ad

d

ep

thSpecial case 2 – completely age dependent

P-F intervalAt least 5000 km

Maximum rate of wear

Tread depth when new = 12 mm

Potential failure = 3 mm

Functional failure = 2 mm

Cross-section of tire tread

Many failure modes are both age and condition indicator dependent. (The age parameter often summarizes the influence of all those wear related factors not explicitly included in the decision / risk model.)


The CBM Decision supported by EXAKT

Given the condition today, the asset mgr. takes one of three decisions:

1. Intervene immediately and conduct maintenance on an equipment at this time, or to

2. Plan to conduct maintenance within a specified time, or to

3. Defer the maintenance decision until the next CBM observation


EXAKT has two ways of deciding whether an item is in a “P” state

1. A decision based solely on failure probability.

2. A decision based on the combination of failure probability and the quantifiable consequences of the failure, and

The two methods

1. Age data2. CM data3. Cost data

Hazard ModelTransition Model

RULEFailure probability

MaintenanceDecision

Cost and Availability Model


History of CBM


The anatomy of CBM

Data Acquisition

Signal Processing

DecisionMaking


Data acquisitionHART (Highway Addressable Remote Transducer)•A backward compatible enhancement to the 4-20mA instrumentation installed in plants today.•Allows two-way communication with the smart microprocessor based field devices that are now commonplace• Carried on the same wires as, and not interrupting the 4-20 mA signal• Provides access to the access to the wealth of information in 12 million HART devices.•Process related variables are transmitted back as an IEEE floating point values with engineering units and data quality assessments.• Supported by all of the major global instrument suppliers

MIMOSA (Machinery Information Management Open System Alliance)•Human-Machine Interfaces (HMI), Manufacturing Execution Systems (MES), Plant Asset Management (PAM) systems, Enterprise Asset Management (EAM) systems, Operational Data Historian Systems (ODHS), and Condition Monitoring (CM) systems. •Common relational information system (CRIS)

OSACBM (Open System Architecture for Condition Based Maintenance)•UML•AIDL•IDL (CORBA, COM/DCOM, XML dotNET)

www.hartcomm.org

www.mimosa.org

www.osacbm.org


Notification logic – CBM trigger

• PI Alarm• PI Performance Equation• PI Advance Computing Engine


The anatomy of CBM

Data Acquisition

Signal Processing

DecisionMaking

b

b

b


Signal Processing


Signal Processing

Failure modes: 1. Shaft Rubs at bearings and seals due to oil whip, 2. coupling misaligned, 3. growth due to thermal effects, 4. lubrication loss, 5. oil contaminated, 6. blade erodes due to wet steam causing charge separation

and cavitation, 7. charge separation and spark discharge due to dry steam at

inlet to turbine with partial admission, 8. shaft grounding lost, 9. intermittent ground fault due to torn copper leaf, 10. insulation shorted at bearings, 11. seals and couplings, 12. stator core lamination shorts, 13. diode fails in generator excitation, 14. excessive transients in pulse width modulated rotor and/or

stator electrical supply

www.gaussbusters.com

U.S. Patent No. 6460013


Signal Processing

Many, ordinarily random signals, when represented in state space

using a branch of mathematics known as Chaos theory, display patterns, deviations from which may be tracked and related to specific modes of failure.


Signal Processing


Active Noise Cancellation (ANC)

1. Adaptive technique to remove noise in real-time

2. The ANC has been successfully applied in

canceling noise during the use of mobile phones

3. Especially suitable for filtering the vibration signal

of a component that has been seriously affected

by vibrations generated from adjacent components

Active Noise Cancellation (ANC)

Interfering signal

Resulting signal reveals the faulty impacts

Primary signal (with interference)

Save data

Peter Tse – SAMS, City University, Hong Kong

The impacts caused by the bearing can be easily identified.


Use of ANC and Wavelet’s Decomposition to Verify the Cause of Bearing Defects

The results can be used to find the cause(s) of bearing defect(s) by matching the interval of impact (around 9ms per impact) as shown in the

display. Bearing (SKF 6215) - Calculated bearing race characteristic frequency at a rotation speed of 25 Hz is 113.6 Hz. Hence, the period of impact caused by a bearing’s race defect should be around 9 ms which is closely matched with the impacts as shown.

Peter Tse – SAMS, City University, Hong Kong


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Where?

Signal processing … What next?

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25/26 61/62 68P-F Intvl


The third sub-process of CBM

Availability

Cost

Mission reliability

Other KPI’s

Residual life estimate 56 days

Decision Making!

What is data?

10


Two major types of data:1.Age (event) data:

2.Condition monitoring data:

1. the beginning of a life-cycle, and2. the ending of a life-cycle:

1. By failure:

2. By suspension, and

1. Potential2. Functional

1.Measurements and inspections2.Process data:

1. External variables2. Internal variables

3. non-rejuvinating events:


Two types of CBM variables

1. External: CBM measurements that detect abnormal stresses on a system that, if uncorrected, will eventually and predictably provoke a failure that has not yet initiated, and

2. Internal: CBM measurements that detect the result of abnormal stresses – that is, they monitor a failure that has already begun, but has not progressed to the point where a required function has been lost.

Sometimes external variables are simple and inexpensive to acquire, and have significant predictive content.


Prediction?

• Failure process has initiated. How much time before functional failure?– High frequency vibration detected.

• Failure process has not yet initiated but will initiate

soon. What is the recommended action now?– Accumulated stress incidences, for example:

water in oil, overloads, cold starts, etc.

Internal

External

If machine stops the variable process stops.

If machine stops the variable process continues.


Advantages of external variables

Randomness, being the rule, rather than the exception, is it reasonable for us to assume that we will usually find a monotonically rising trend of some monitored variable throughout a component’s lifecycle, from which we may predict its failure?

A reasonable approach to CBM would be also to monitor the equipment and its operating context for signs of external conditions causing abnormal stress, which, if allowed to persist, will be destructive. Doctors monitor cholesterol to determine whether our arteries are in danger of clogging. At a certain level, they order a corrective action, usually a change in lifestyle. Maintainers monitor oil levels to avoid the consequences of over- or under-lubrication. Vibration analysts determine a condition of foundation weakness, shaft misalignment or of rotor imbalance, which, if uncorrected, will lead to serious failure.

CBM Optimization

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cbm decision making

Documents

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