Failure Models Derived Through the Indifference Principle (UCB-ENG-8293).
Abstract
This draft of a new book entitled ENGINEERING RELIABILITY concerns failure data analysis, the economics of maintenance policies and system reliability. The purpose of this book is to develop the use of probability in engineering reliability and maintenance problems. We use probability models in the (1) analysis of failure data; (2) decision relative to planned maintenance; and (3) prediction relative to preliminary design. Engineering applications are emphasized and are used to motivate the methodology presented. Part I is devoted to the analysis of failure data, particularly lifetime data and failure counts. We begin by using a new approach to probability applications. The approach starts with finite populations and derives conditional probability models based on engineering and economic considerations. Infinite population conditional probability models most often used are approximations to these finite population models. The derived conditional probability models are then the basis for likelihood functions useful for the analysis of failure data. Part II is devoted to the economics of maintenance decisions. We begin with the economics of replacement decisions. Emphasis is on the time value of money and discounting. Then we consider inspection policies relative to operating systems and production sampling. Part Ill is devoted to system reliability. We begin with efficient algorithms for computing network reliability. Networks or block diagrams are abstract system representations useful for both reliability prediction and maintenance considerations. Availability and maintainability formulas are derived and used in applications. Fault tree analysis as presented is one of the most useful tools in identifying system failure modes and effects.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1996
- Accession Number
- ADA315265
Entities
People
- Richard Barlow
Organizations
- University of California, Berkeley