Maintenance, Replacement, and Reliability; Theory and Applications (3rd Edition)

Since the publication of the second edition in 2013, there has been an increasing interest in asset management globally, as evidenced by a series of international standards on asset management systems, to achieve excellence in asset management. This cannot be achieved without high-quality data and the tools for data interpretation. The importance of such requirements is widely recognized by industry. The third edition of this textbook focuses on tools for physical asset management decisions that are data driven. It also uses a theoretical foundation to the tools (mathematical models) that can be used to optimize a variety of key maintenance/replacement/reliability decisions. Problem sets with answers are provided at the end of each chapter. Also available is an extensive set of PowerPoint slides and a solutions manual upon request with qualified textbook adoptions. This new edition can be used in undergraduate or post-graduate courses on physical asset management. Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface for the First Edition Preface for the Second Edition Preface for the Third Edition Acknowledgments for the First Edition Acknowledgments for the Second Edition Acknowledgments for the Third Edition Author Biographies Chapter 1: Introduction 1.1 From Maintenance Management to Physical Asset Management 1.2 Challenges of PAM 1.2.1 Emerging Trends of Operation Strategies 1.2.2 Toughening Societal Expectations 1.2.3 Technological Changes 1.2.4 Increased Emphasis on Sustainability 1.3 Improving PAM 1.3.1 Maintenance Excellence 1.3.1.1 Strategic 1.3.1.2 Tactical 1.3.1.3 Continuous Improvements 1.3.2 Quantum Leaps 1.4 A Framework for Optimized Management of Physical Assets 1.4.1 ISO 55000 Standards 1.4.2 Asset Management Maturity Assessment 1.5 Reliability through the Operator: TPM 1.6 Reliability by Design: RCM 1.7 Optimizing Maintenance and Replacement Decisions 1.8 The Quantitative Approach 1.8.1 Setting Objectives 1.8.2 Models 1.8.3 Obtaining Solutions from Models 1.8.4 Maintenance Control and Mathematical Models 1.9 Data Requirements for Modeling References Chapter 2: Component Replacement Decisions 2.1 Introduction 2.2 Optimal Replacement Times for Equipment Whose Operating Cost Increases with Use 2.2.1 Statement of the Problem 2.2.2 Construction of the Model 2.2.3 Numerical Example 2.2.4 Further Comments 2.2.5 Applications 2.2.5.1 Replacing the Air Filter in an Automobile 2.2.5.2 Overhauling a Boiler Plant 2.3 Stochastic Preventive Replacement: Some Introductory Comments 2.4 Optimal Preventive Replacement Interval of Items Subject to Breakdown (Also Known as the Group or Block Policy) 2.4.1 Statement of the Problem 2.4.2 Construction of the Model 2.4.3 Determination of H (t) 2.4.3.1 Renewal Theory Approach 2.4.3.2 Discrete Approach 2.4.4 Numerical Example 2.4.5 Further Comments 2.4.6 An Application: Optimal Replacement Interval for a Left-ŁHand Steering Clutch 2.5 Optimal Preventive Replacement Age of an Item Subject to Breakdown 2.5.1 Statement of the Problem 2.5.2 Construction of the Model 2.5.3 Numerical Example 2.5.4 Further Comments 2.5.5 An Application: Optimal Bearing Replacement Age 2.6 Optimal Preventive Replacement Age of an Item Subject to Breakdown, Taking Account of the Times Required to Carry Out Failure and Preventive Replacements 2.6.1 Statement of the Problem 2.6.2 Construction of the Model 2.6.3 Numerical Example 2.7 Optimal Preventive Replacement Interval or Age of an Item Subject to Breakdown: Minimization of Downtime 2.7.1 Statement of the Problem 2.7.2 Construction of the Models 2.7.2.1 Model 1: Determination of Optimal Preventive Replacement Interval 2.7.2.2 Model 2: Determination of Optimal Preventive Replacement Age 2.7.3 Numerical Examples 2.7.3.1 Model 1: Replacement Interval 2.7.3.2 Model 2: Replacement Age 2.7.4 Further Comments 2.7.5 Applications 2.7.5.1 Replacement of Sugar Refinery Cloths 2.7.5.2 Replacement of Sugar Feeds in a Sugar Refinery 2.8 Group Replacement: Optimal Interval between Group Replacements of Items Subject to Failure— the Lamp Replacement Problem 2.8.1 Statement of the Problem 2.8.2 Construction of the Model 2.8.3 Numerical Example 2.8.4 Further Comments 2.8.5 An Application: Optimal Replacement Interval for a Group of 40 Valves in a Compressor 2.9 Further Replacement Models 2.9.1 Multistage Replacement 2.9.2 Optional Policies 2.9.3 Repairable Systems 2.10 Case Study on Project Prioritization, Trend Tests, Weibull Analysis, and Optimizing Component Replacement Intervals 2.10.1 Introduction 2.10.2 Optimal Preventive Replacement Age for Major Components 2.10.3 Optimal Preventive Replacement Age for Item Parts (Minor Components) 2.10.4 Conclusion for Item Parts 2.11 Spare Parts Provisioning: Preventive Replacement Spares 2.11.1 Introduction 2.11.2 Construction of the Model 2.11.2.1 The Constant Interval Model 2.11.2.2 The Age-ŁBased Preventive Replacement Model 2.11.3 Numerical Example 2.11.3.1 Constant-Interval Policy 2.11.3.2 Age-Based Policy 2.11.4 Further Comments 2.11.5 An Application: Cylinder Head Replacement—Constant-Interval Policy 2.12 Spare Parts Provisioning: Insurance Spares 2.12.1 Introduction 2.12.2 Classes of Components 2.12.2.1 Nonrepairable Components 2.12.2.2 Normal Distribution Approach 2.12.2.3 Poisson Distribution Approach 2.12.2.4 Repairable Components 2.12.3 Cost Model 2.12.4 Further Comments 2.12.5 An Application: Electric Motors 2.13 Solving the Constant-Interval and Age-Based Models Graphically: Use of Glasser’s Graphs 2.13.1 Introduction 2.13.2 Using Glasser’s Graphs 2.13.3 Numerical Example 2.13.4 Calculation of the Savings 2.14 Solving the Constant-Interval and Age-Based Models Using OREST Software 2.14.1 Introduction 2.14.2 Using OREST 2.14.3 Further Comments Problems References Chapter 3: Inspection Decisions 3.1 Introduction 3.2 Optimal Inspection Frequency: Maximization of Profit 3.2.1 Statement of the Problem 3.2.2 Construction of the Model 3.2.3 Numerical Example 3.2.4 Further Comments 3.3 Optimal Inspection Frequency: Minimization of Downtime 3.3.1 Statement of the Problem 3.3.2 Construction of the Model 3.3.3 Numerical Example 3.3.4 Further Comments 3.3.5 An Application: Optimal Vehicle Fleet Inspection Schedule 3.4 Optimal Inspection Interval to Maximize the Availability of Equipment Used in Emergency Conditions, Such as a Protective Device 3.4.1 Statement of the Problem 3.4.2 Construction of the Model 3.4.3 Numerical Example 3.4.4 Further Comments 3.4.5 Exponential Failure Distribution and Negligible Time Required to Perform Inspections and Repairs/Replacements 3.4.6 An Application: Pressure Safety Valves in an Oil and Gas Field 3.5 Optimizing CBM Decisions 3.5.1 Introduction 3.5.2 The Proportional Hazards Model 3.5.3 Blending Hazard and Economics: Optimizing the CBM Decision 3.5.4 Applications 3.5.4.1 Food Processing: Use of Vibration Monitoring 3.5.4.2 Coal Mining: Use of Oil Analysis 3.5.4.3 Transportation: Use of Visual Inspection 3.5.5 Further Comments 3.5.6 Software for CBM Optimization 3.5.6.1 Event Data 3.5.6.2 Data Management Issues Problems References Chapter 4: Capital Equipment Replacement Decisions 4.1 Introduction 4.2 Optimal Replacement Interval for Capital Equipment: Minimization of Total Cost 4.2.1 Statement of the Problem 4.2.2 Construction of the Model 4.2.3 Numerical Example 4.2.4 Further Comments 4.2.5 Applications 4.2.5.1 Mobile Equipment: Vehicle Fleet Replacement 4.2.5.2 Fixed Equipment: Internal Combustion Engine 4.3 Optimal Replacement Interval for Capital Equipment: Maximization of Discounted Benefits 4.3.1 Statement of the Problem 4.3.2 Construction of the Model 4.3.2.1 First Cycle of Operation 4.3.2.2 Second Cycle of Operation 4.3.2.3 Third Cycle of Operation 4.3.2.4 n th Cycle of Operation 4.3.3 Numerical Example 4.3.4 Further Comments 4.3.5 Proof that Optimization Over a Long Period Is Not Equivalent to Optimization per Unit Time When Discounting Is Included 4.4 Optimal Replacement Interval for Capital Equipment Whose Planned Utilization Pattern Is Variable: Minimization of Total Cost 4.4.1 Statement of the Problem 4.4.2 Construction of the Model 4.4.2.1 Consider a Replacement Cycle of n Years 4.4.3 Numerical Example 4.4.4 Further Comments 4.4.5 An Application: Establishing the Economic Life of a Fleet of Buses 4.5 Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Finite Planning Horizon 4.5.1 Statement of the Problem 4.5.2 Construction of the Model 4.5.3 Numerical Example 4.5.4 Further Comments 4.5.5 An Application: Replacing Current Mining Equipment with a Technologically Improved Version 4.6 Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Infinite Planning Horizon 4.6.1 Statement of the Problem 4.6.2 Construction of the Model 4.6.3 Numerical Example 4.6.4 Further Comments 4.6.5 An Application: Repair versus Replace of a Front-End Loader 4.7 Sustainable Management of a Fleet of Assets: Life Cycle Costing and Greenhouse Gas (GHG) emissions 4.7.1 Statement of the Problem 4.7.2 The Triple Bottom Line 4.7.3 Greenhouse Gas Reduction and Sustainable Development 4.7.4 Conclusion 4.8 Software for Economic Life Optimization 4.8.1 Introduction 4.8.3 Further Comments 4.8.2 Using PERDEC and AGE/CON Problems References Chapter 5: Maintenance Resource Requirements 5.1 Introduction 5.1.1 Facilities for Maintenance within an Organization 5.1.2 The Combined Use of the Facilities within an Organization and Outside Resources 5.2 Queuing Theory Preliminaries 5.2.1 Queuing Systems 5.2.2 Queuing Theory Results 5.2.2.1 Single-Channel Queuing System 5.2.2.2 Multichannel Queuing Systems 5.3 Optimal Number of Workshop Machines to Meet a Fluctuating Workload 5.3.1 Statement of the Problem 5.3.2 Construction of the Model 5.3.3 Numerical Example 5.3.4 Further Comments 5.3.5 Applications 5.3.5.1 Optimizing the Backlog 5.3.5.2 Crew Size Optimization 5.4 Optimal Mix of Two Classes of Similar Equipment (such as Medium/Large Lathes) to Meet a Fluctuating Workload 5.4.1 Statement of the Problem 5.4.2 Construction of the Model 5.4.2.1 Logic Flowchart 5.4.2.2 Obtaining Necessary Information and Constructing the Model 5.4.3 Numerical Example 5.4.4 Further Comments 5.4.5 Applications 5.4.5.1 Establishing the Optimal Number of Lathes in a Steel Mill 5.4.5.2 Balancing Maintenance Cost and Reliability in Thermal Generating Station 5.5 Rightsizing a Fleet of Equipment: An Application 5.5.1 An Application: Fleet Size in an Open-Pit Mine 5.6 Optimal Size of a Maintenance Workforce to Meet a Fluctuating Workload, Taking Account of Subcontracting Opportunities 5.6.1 Statement of the Problem 5.6.2 Construction of the Model 5.6.3 Numerical Example 5.6.4 Further Comments 5.6.5 An Example: Number of Vehicles to Have in a Fleet (Such as a Courier Fleet) 5.7 The Lease or Buy Decision 5.7.1 Statement of the Problem 5.7.2 Solution of the Problem 5.7.2.1 Use of Retained Earnings 5.7.2.2 Use of Borrowed Funds 5.7.2.3 Leasing 5.7.2.4 Conclusion 5.7.3 Further Comments Problems References Chapter 6: The Role of Emerging Technologies in Physical Asset Management 6.1 Introduction 6.2 Artificial Intelligence 6.2.1 Machine Learning and Deep Learning 6.2.1.1 Supervised Learning 6.2.1.2 Unsupervised Learning 6.2.1.3 Semi-Supervised Learning 6.2.1.4 Reinforced Learning 6.2.2 Applications of Machine Learning in Physical Asset Management 6.2.2.1 Fault Detection and Diagnosis 6.2.2.2 Predictive Maintenance 6.2.2.3 Other Applications of ML in Physical Asset Management 6.3 Internet of Things (IoT) 6.3.1 Automation 6.3.2 Innovation 6.3.3 Digital Transformation 6.3.4 Conclusion 6.4 Industry 4.0 and Predictive Maintenance 4.0 6.4.1 Predictive Maintenance 4.0 6.5 Digital Twin 6.5.1 Creating a Digital Twin 6.5.2 Application of Digital Twins in Physical Asset Management 6.6 Blockchain 6.6.1 Benefits of Blockchains 6.6.2 Smart Contracts 6.6.3 Asset Tokenization 6.6.4 Application of Blockchains in Physical Asset Management 6.7 Further Comments Problems References Appendix 1: Statistics Primer A1.1 Introduction A1.2 Relative Frequency Histogram A1.3 Probability Density Function A1.3.1 Hyperexponential A1.3.2 Exponential A1.3.3 Normal A1.3.4 Weibull A1.4 Cumulative Distribution Function A1.5 Reliability Function A1.6 Hazard Rate A1.7 The Accompanying E-Learning Materials Problems Reference Further Reading Appendix 2: Weibull Analysis A2.1 Weibull Distribution A2.1.1 Shape Parameter A2.1.2 Scale Parameter A2.1.3 Location Parameter A2.1.4 Fitting a Distribution Model to Sample Data A2.2 Weibull Paper A2.3 Weibull Plot A2.3.1 Estimating the Cumulative Percentage of Failure, F(T) A2.3.2 Estimating the Parameters A2.3.3 Nonlinear Plot A2.4 Confidence Interval of a Weibull Plot A2.5 Bq LIFE A2.6 Kolmogorov–Smirnov Goodness-of-Fit Test A2.7 Analyzing Failure Data with Suspensions A2.8 Analyzing Grouped Failure Data with Multiple Suspensions A2.9 Analyzing Competing Failure Data A2.10 Hazard Plot A2.10.1 Nonlinear Plot A2.11 Other Approaches to Weibull Analysis A2.12 Analyzing Trends of Failure Data A2.12.1 Machine H A2.12.2 Machine S A2.13 The Accompanying E-Learning Materials Problems References Further Reading Appendix 3: Maximum Likelihood Estimator A3.1 The Method A3.2 Maximum Likelihood Estimator for Parameters of an Exponential Distribution A3.3 Application to Life Testing A3.4 Maximum Likelihood Estimator for Parameters of a Weibull Distribution Reference Appendix 4: Markov Chains A4.1 Defining a Markov Chain A4.2 N-Step Transition Probabilities A4.3 Limiting State Probabilities A4.4 Mean First-Passage Times A4.5 Fitting a Markov Chain Model A4.6 Markov Chains with Rewards Further Reading Appendix 5: Knowledge Elicitation A5.1 Introduction A5.2 Knowledge Elicitation A5.3 Combining Expert Knowledge with Data A5.4 Numerical Example A5.5 Application Examples A5.5.1 Compressors A5.5.2 Fleet of Station Transformers A5.6 Further Comments References Further Reading Appendix 6: Time Value of Money Discounted Cash Flow Analysis A6.1 Introduction A6.2 Present Value Formulas A6.3 Determination of Appropriate Interest Rate A6.4 Inflation A6.5 Equivalent Annual Cost A6.6 Example: Selecting an Alternative—​A One-Shot Decision A6.7 Further Comments References Appendix 7: List of Applications of Asset Management Decision Optimization Models Appendix 8: Ordinates of the Standard Normal Distribution Appendix 9: Areas in the Tail of the Standard Normal Distribution Appendix 10: Values of Gamma Function Appendix 11: Median Ranks Table Appendix 12: Five Percent Ranks Table Appendix 13: Ninety-Five Percent Ranks Table Appendix 14: Critical Values for the Kolmogorov– Smirnov Statistic (dα) Appendix 15: Answers to Problems Chapter 2: Component Replacement Decisions Chapter 3: Inspection Decisions Chapter 4: Capital Equipment Replacement Decisions Chapter 5: Maintenance Resource Requirements Chapter 6: The Role of Emerging Technologies in Physical Asset Management Appendix 1: Statistics Primer Appendix 2: Weibull Analysis Index Based on results of research in physical asset management, this book offers the necessary tools for making data-driven decisions. Applications are demonstrated by way of case studies, in areas including food processing, petrochemical, military, mining, transportation, steel and pharmaceutical industries. Since the publication of the second edition in 2013, there has been an increasing interest in asset management globally, as evidenced by a series of international standards on asset management systems, to achieve excellence in asset management. This cannot be achieved without high-quality data and the tools for data interpretation. The importance of such requirements is widely recognized by industry. The third edition of this textbook focuses on tools for physical asset management decisions that are data driven. It also uses a theoretical foundation to the tools (mathematical models) that can be used to optimize a variety of key maintenance/replacement/reliability decisions. Problem sets with answers are provided at the end of each chapter. Also available is an extensive set of PowerPoint slides and a solutions manual upon request with qualified textbook adoptions. This new edition can be usedin undergraduate or post-graduate courses on physical asset management "Since the publication of the second edition in 2013, there have been an increasing interest in asset management globally as evidenced by a series of international standards on asset management systems, to achieve excellence in asset management. This cannot be achieved without high quality data and the tools for data interpretation. The importance of such requirements is widely recognized by industry. The third edition of this textbook focuses on tools for physical asset management decisions that are data driven. It also uses a theoretical foundation to the tools (mathematical models) that can be used to optimize a variety of key maintenance/replacement/reliability decisions. Problem sets with answers are provided at the end of each chapter. Also available is an extensive set of PowerPoint slides and a solutions manual upon request with qualified textbook adoptions. The new edition can be used by undergraduate or post graduate courses on physical asset management"-- Provided by publisher