2. CBM Decision making P-F Interval Optimized CBM decisions History, Anatomy, Nature of data

3. OMDEC Optimal Maintenance Decisions Inc. 2 Two Bearings 1/3.5 1/7 Warning 2 days OK Failed P-F = 2 Days Noise starts 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 Bearings Risk Conditional Probability of Failure Noise starts OK Warning 2 wks Failed P-F = 2 Weeks Functional performance

4. OMDEC Optimal Maintenance Decisions Inc. 3 Initial inspection interval Is CBM for the failure mode in questionapplicable?(Is there a clearly identifiable condition indicator? Is the warning time adequate?) Is CBM for the failure mode in questioneffective? (Is there an economical CBM task and interval that will avoid or reduce, to a tolerable level, the consequences of the failure?) NO YES is the warning period of the order 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 not effective -> Descend to next task type in the RCM algorithm. NO YES

5. OMDEC Optimal Maintenance Decisions Inc. 4 * The Elusive P-F Interval Condition Working Age Potential Failure P P-F Interval Warning Interval Failure F Inspection Interval Inspection data * Ideal ? ? ? Real

6. OMDEC Optimal Maintenance Decisions Inc. 5 The conventional CBM decision method from Nowlan & Heap, (Moubray) Condition 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

7. OMDEC Optimal Maintenance Decisions Inc. 6 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

8. OMDEC Optimal Maintenance Decisions Inc. 7 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.random variables 3.P may not be constant for different working ages of the item.

9. Moubray (RCM II) addresses two extreme cases F2F2 F3F3 F1F1 023451 Failures occur on a random basis Inspections at 2 month intervals PF detected at least 2 months before FF. PF FF Age (years) Special case 1 – completely random (age independent, dependent only on condition monitoring data) 02030405010 Operating Age (x 1000 km) PF FF Tread depth Special case 2 – completely age dependent P-F interval At 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.)

10. OMDEC Optimal Maintenance Decisions Inc. 9 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

11. OMDEC Optimal Maintenance Decisions Inc. 10 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

12. The two methods 1.Age data 2.CM data 3.Cost data Hazard Model Transition Model RULE Failure probability Maintenance Decision Cost and Availability Model

13. OMDEC Optimal Maintenance Decisions Inc. 12 History of CBM

14. OMDEC Optimal Maintenance Decisions Inc. 13 The anatomy of CBM Data Acquisition Signal Processing Decision Making

15. OMDEC Optimal Maintenance Decisions Inc. 14 Data acquisition HART (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

16. OMDEC Optimal Maintenance Decisions Inc. 15 Notification logic – CBM trigger PI Alarm PI Performance Equation PI Advance Computing Engine

17. OMDEC Optimal Maintenance Decisions Inc. 16 The anatomy of CBM Data Acquisition Signal Processing Decision Making b b b

18. OMDEC Optimal Maintenance Decisions Inc. 17 Signal Processing

19. OMDEC Optimal Maintenance Decisions Inc. 18 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

20. OMDEC Optimal Maintenance Decisions Inc. 19 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.

21. OMDEC Optimal Maintenance Decisions Inc. 20 Signal Processing

22. OMDEC Optimal Maintenance Decisions Inc. 21 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

23. 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.

24. OMDEC Optimal Maintenance Decisions Inc. 23 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

25. OMDEC Optimal Maintenance Decisions Inc. 24 10 Where? Signal processing … What next? 10 25/26 61/62 68 P-F Intvl

26. OMDEC Optimal Maintenance Decisions Inc. 25 The third sub-process of CBM Availability Cost Mission reliability Other KPI’s Residual life estimate 56 days Decision Making!

27. What is data? 10

28. OMDEC Optimal Maintenance Decisions Inc. 27 Two major types of data: 1.Age (event) data: 2.Condition monitoring data: 1.the beginning of a life-cycle, and 2.the ending of a life-cycle: 1.By failure: 2.By suspension, and 1.Potential 2.Functional 1.Measurements and inspections 2.Process data: 1.External variables 2.Internal variables 3. non-rejuvinating events:

29. OMDEC Optimal Maintenance Decisions Inc. 28 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.

30. OMDEC Optimal Maintenance Decisions Inc. 29 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.

31. OMDEC Optimal Maintenance Decisions Inc. 30 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.

32. CBM Optimization 10

33. OMDEC Optimal Maintenance Decisions Inc. 32 The Traditional CBM Model Working Age Condition Indicator 1.Indicator reflects a failure mode degradation process. 2.The alarm limit is constant with age Two Assumptions

34. OMDEC Optimal Maintenance Decisions Inc. 33 DATA PLOT RISK PLOT Age Data Age Risk Data and Risk

35. OMDEC Optimal Maintenance Decisions Inc. 34 At what level of risk to we want to “maintain” an asset? High Risk Cost/unit time Cost Lowest cost Availability Highest availability Reliability Specified reliabiltity High (low) Risk laissez-faire conservative

36. OMDEC Optimal Maintenance Decisions Inc. 35 Typical Life of a Component B = beginning of component life EF = ending with failure B EFEF B EFEF B EFEF B EFEF EFEF B EFEF B

37. OMDEC Optimal Maintenance Decisions Inc. 36 No Scheduled Renewal Policy CFCF CFCF CFCF CFCF CFCF CFCF 0

38. OMDEC Optimal Maintenance Decisions Inc. 37 Preventive Renewal Policy B t1t1 t3t3 t4t4 t5t5 t6t6 tBtB

39. OMDEC Optimal Maintenance Decisions Inc. 38 tBtB Preventive renewal policy C t1t1 t3t3 t4t4 t5t5 t6t6

40. Q. Where to put the vertical line? POSSIBLE DECISION POLICIES: Policy A: Policy C: Policy B: Policy B Policy C t A = 2,500h t B = 4,000h t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 Policy “At Failure Only” Policy A cost per working hour: Policy B, cost per working hour: Policy C, cost per working hour: no scheduled maintenance: 4000h 2500h What does it depend on? Policy A

41. OMDEC Optimal Maintenance Decisions Inc. 40 Learning from data Sample tBtB tCtC

42. OMDEC Optimal Maintenance Decisions Inc. 41 Two Questions 1.How does its failure risk vary with its age (and CM readings)? and, 2.How do we combine the costs, C P and C F, with failure risk to arrive at an optimal decision?

43. OMDEC Optimal Maintenance Decisions Inc. 42 The failure probability Probability of failure Time

44. OMDEC Optimal Maintenance Decisions Inc. 43 Parameter Estimates

45. OMDEC Optimal Maintenance Decisions Inc. 44 The software finds the t p which minimizes the cost C t f(t) tptp F(t) R(t) Where to put the vertical line? Or what is the best time t p to do PM? Area R(t) is the probability that the item will survive to time t. Area F(t) is the probability of failure at or before time t. Eqn 1 Eqn 2 Eqn 3

46. OMDEC Optimal Maintenance Decisions Inc. 45 Does this answer question posed earlier? tptp