Reliability is a highly quantitative engineering discipline. Statistical models and methods play an important role in reliability. In particular, many reliability‐related decisions require the analysis of reliability data, either from past field performance or from laboratory tests (usually accelerated tests). For example, prediction of system reliability during the system or product design phase requires a system‐level model specifying the system's life‐limiting components and system structure. Important inputs for this model include information on the reliabilities of the subsystems and components of the system. In many cases such inputs are available from handbooks (e.g., MIL‐HDBK‐217F or the Bellcore, Telcordia, Standard TR‐332). When component reliability information is not available from such sources (e.g., because of the introduction of a new material or component or the use of an old component or material in a new environment), testing to assure reliability may be necessary and statistical methods are needed to do proper analyses of reliability data.
This chapter describes statistical methods that are used in the analysis of reliability data. We will describe methods for analyzing system‐level data that may be useful to compare different systems or to track changes in system reliability over time (perhaps due to system deterioration or reliability improvement efforts). Most of the focus, however, will be on methods that are commonly used in reliability‐for‐design programs or design‐for‐Six‐Sigma programs where the goal is to design a product that will meet specific reliability goals.
Reliability
System Health Management ; 233-251
2011-07-15
19 pages
Article/Chapter (Book)
Electronic Resource
English
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