Details
Firmly based on the results of real-world research in physical asset management, the book focuses on data-driven tools for asset management decisions. It provides a solid foundation for various tools that can be used to optimize a variety of key maintenance/replacement/reliability decisions. It presents cases that illustrate the application of these tools in a variety of settings, such as food processing, petrochemical, steel and pharmaceutical industries, as well as the military, mining, and transportation (land and air) sectors.
The book includes models relating to spare-parts provisioning, condition-based maintenance, and replacement of equipment with varying levels of utilization.
Author: | Jardine and Tsang |
Published: | 2013 |
Format: | Hardback |
Pages: | 364 |
Table Of Contents: | Introduction From Maintenance Management to Physical Asset Management Challenges of PAM Improving PAM PAS 55—A Framework for Optimized Management of Physical Assets Reliability through the Operator: TPM Reliability by Design: RCM Optimizing Maintenance and Replacement Decisions The Quantitative Approach Data Requirements for Modeling References Component Replacement Decisions Introduction Optimal Replacement Times for Equipment Whose Operating Cost Increases with Use Stochastic Preventive Replacement: Some Introductory Comments Optimal Preventive Replacement Interval of Items Subject to Breakdown (Also Known as the Group or Block Policy) Optimal Preventive Replacement Age of an Item Subject to Breakdown Optimal Preventive Replacement Age of an Item Subject to Breakdown, Taking Account of the Times Required to Carry Out Failure and Preventive Replacements Optimal Preventive Replacement Interval or Age of an Item Subject to Breakdown: Minimization of Downtime Group Replacement: Optimal Interval between Group Replacements of Items Subject to Failure: the Lamp Replacement Problem Further Replacement Models Case Study on Project Prioritization, Trend Tests, Weibull Analysis, and Optimizing Component Replacement Intervals Spare Parts Provisioning: Preventive Replacement Spares Spare Parts Provisioning: Insurance Spares Solving the Constant-Interval and Age-Based Models Graphically: Use of Glasser’s Graphs Solving the Constant-Interval and Age-Based Models Using OREST Software References
Inspection Decisions Introduction Optimal Inspection Frequency: Maximization of Profit Optimal Inspection Frequency: Minimization of Downtime Optimal Inspection Interval to Maximize the Availability of Equipment Used in Emergency Conditions, Such as a Protective Device Optimizing CBM Decisions References
Capital Equipment Replacement Decisions Introduction Optimal Replacement Interval for Capital Equipment: Minimization of Total Cost Optimal Replacement Interval for Capital Equipment: Maximization of Discounted Benefits Optimal Replacement Interval for Capital Equipment Whose Planned Utilization Pattern Is Variable: Minimization of Total Cost Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Finite Planning Horizon Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Infinite Planning Horizon Software for Economic Life Optimization References
Maintenance Resource Requirements Introduction Queuing Theory Preliminaries Optimal Number of Workshop Machines to Meet a Fluctuating Workload Optimal Mix of Two Classes of Similar Equipment (such as Medium/Large Lathes) to Meet a Fluctuating Workload Rightsizing a Fleet of Equipment: An Application Optimal Size of a Maintenance Workforce to Meet a Fluctuating Workload, Taking Account of Subcontracting Opportunities The Lease or Buy Decision References
Appendices: Statistics Primer Weibull Analysis Maximum Likelihood Estimator Markov Chains Knowledge Elicitation Time Value of Money: Discounted Cash Flow Analysis List of Applications of Maintenance Decision Optimization Models Ordinates of the Standard Normal Distribution Areas in the Tail of the Standard Normal Distribution Values of Gamma Function Median Ranks Table Five Percent Ranks Table Ninety-Five Percent Ranks Table Critical Values for the Kolmogorov–Smirnov Statistic (dα) Answers to Problems Index |
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