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Reliability-centered Knowledge Using Maintenance Databases for Reliability Analysis and Improvement Part 1. A Reliability-centered Knowledge Base 6 Part 2. Using Maintenance Data 21 Part 3. Condition Based Maintenance 49 Part 4. Reliability Centered Maintenance 110 by: Murray Wiseman Technical V.P. Optimal Maintenance Decisions (OMDEC) Inc. Mar 2004 Page 2 Optimal Maintenance Decisions (OMDEC) Inc ©2004
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    Reliability-centered Knowledge Using Maintenance Databases for Reliability Analysis and Improvement Part 1. A Reliability-centered Knowledge Base 6 Part 2. Using Maintenance Data 21 Part 3. Condition Based Maintenance 49 Part 4. Reliability Centered Maintenance 110 by: Murray Wiseman Technical V.P. Optimal Maintenance Decisions (OMDEC) Inc. Mar 2004     Page 2 Optimal Maintenance Decisions (OMDEC) Inc © 2004 Preface This book provides the course notes for a CBM (condition based monitoring) training session that consists of 3 parts: 1. Database attributes that are required for reliability analysis, 2. Using the information from such databases, and particularly, 3. Interpreting in an optimal way the data generated by condition based maintenance activities. Material for a fourth part, “Reliability-centered maintenance” (RCM), is appended because the principles of EXAKT are founded in RCM theory. Hence the course draws liberally from concepts such as “failure modes” , “decision analysis”, and “age exploration” that are examined in Part 4. Parts 1 and 2 usually fill the first morning of the course providing the introduction and background for EXAKT. Part 3 begins with a theoretical development of CBM, a history of CBM, and a discussion of the reasons for selecting CBM as a  proactive task. The second section of Part 3 presents the anatomy of CBM, specifically its three sub-processes – data acquisition, signal processing, and decision making. The latter leads naturally into the introduction of EXAKT CBM decision optimization. The fundamentals of CBM are explored further and the RCM concept of the “P-F interval” 1  is reconciled with the methodology of EXAKT. The development of the relationship among data, risk, and cost ensues, using a time-based maintenance example. This approach is shown then to be extendable to CBM using the Weibull PHM 2  model. The need for automated decision making, as a consequence of the growing volumes of data and the diminishing resources that characterize today’s maintenance departments is expounded upon. At this point participants (or readers) are invited to work through an introductory exercise during which they encounter most of the basic features of EXAKT. This includes the 5 principal database tables and their table structure. They proceed to  build a decision model using a reduced set of haul truck data. In the exercise that follows, they deploy the model that they have previously created. That is, they set up an (EXAKTd) intelligent agent and examine its automated analysis, reporting, and database functionality.  Next, the issue of data validation is explored. The example is from a CBM project at the Cardinal River Coals mine in which invalid data, missing data, faulty failure definition, the impact of oil changes on oil analysis data, and cost sensitivity analysis are all encountered, and their respective EXAKT functions explored. This discussion is then reinforced by an exercise in which all of the results of the Cardinal River Coals project are replicated by the class working in  pairs on their own laptops. The exercise includes an introduction to general (data) transformations 3  in EXAKT.   Page 3 Optimal Maintenance Decisions (OMDEC) Inc © 2004 At this time, an advanced topic is introduced – the analysis of complex items 4 . A complex item is defined and the data structure for representing complex items in a model is described. The necessity to map database fields for a variety of components to EXAKT’s key fields of B, EF, and ES (Beginning, Ending by Failure, and Ending by Suspension) is elaborated at some length and then reinforced immediately with an exercise using a two-failure-mode gearbox as an example. The final exercise provides an introduction and practice in the use of history specific (data) transformations, for the purpose of smoothing erratic data. Additional sophistication is demonstrated via the elimination of a “drooping” artifact as a result of the basic smoothing algorithm. Additionally this final exercise introduces the testing of the shape-factor-equal-to-one 5  hypothesis, and the reasoning behind its use in this specific case. This ends the formal part of the course. The attendees are then asked to search their respective records and databases for potentially good CBM optimization projects. The criteria for “good” is articulated in the form of a balanced compromise between availability of inspection and event data on the one hand, and, the gravity of the consequences of failure on the other.   Page 4 Optimal Maintenance Decisions (OMDEC) Inc © 2004 Contents: Part 1.   A Reliability-centered Knowledge Base 6    Chapter 1. 6    Introduction 6   The Work Order UML Class Diagram 7   Incorporating RCM attributes 8  The Seven RCM Questions 9 The “failure code” problem 10   Chapter 2.   Requirements of Information 11    Data Structure 13   Implementing a Reliability Knowledge Base 14  Step 4 Extending the Use Case if no record is found 16 Conclusions 19   Part 2.   Using Maintenance Data 21    Chapter 3.   Analyzing data 21    Introduction 21   The problem with failure rates 22   How to use maintenance data? 23   Age Exploration Procedures 25  Random Failure 26 Failure Finding Intervals 26 Measuring Reliability Improvement 29 Refining the maintenance program 30 Extending inspection intervals where no experience is available – opportunity sampling 31 Assessing the effectiveness of a CBM Program 31 Improving the program through failure mode assessment 32 Software analytic tools 33 CBM (on-condition maintenance) benefits analysis 36 Engineering Change Assessment 40 Recording Events 41 Component age 41 Significant components 42 Chapter 4.   Case based reasoning 43   Results of case-based reasoning 47 Part 3.   Condition Based Maintenance 49    Chapter 5.   Deciding on CBM? 49    Introduction 49   Why do CBM? 50   History of CBM 53   Chapter 6.   Anatomy of CBM 57    Data Acquisition 57   Signal Processing 57   Decision Making 62   Chapter 7.   CBM Fundamentals 64   
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