Managing Reliability Growth in Engineering Design: Decisions, Data and Modelling

The authors of this book have been involved in a UK DTI funded project for 6 years, with the aim of developing a model for reliability growth during system design and development. Working with engineers across major aerospace companies including BAE Systems and Rolls Royce, they have translated modelling theory into practice with an integrated methodology based on a stochastic model. This methodology discusses modelling features including failure process, dependencies, data classification taxonomies, qualitative structuring and quantitative instantiation, and parametric/ non-parametric bootstrapping. Though founded on research conducted in the aerospace sector, the methodology is relevant to all applications of systems engineering in industries such as automotive, process, marine and telecommunications, and is based on a strong understanding of the needs of engineers. In-depth case studies presented in the book elucidate the theory in practice; chapters include modelling processes grounded in decisions and data, formulating the stochastic model, data structures, elicitation of subjective judgment, parameter estimation and updating, decision support and verification and validation. * Translates theory into practice, with a focus on the understanding of practical engineering needs * Discusses practical modelling strategies as well as simply describing techniques * Considers the modelling of reliability growth as an integral part of the systems engineering design process, in both closed control systems and waterfall design processes * Develops methods for preparing and collecting data, including hard failure experience and the elicitation of soft engineering judgment * Presents the formulation of the model under both classical and Bayesian philosophies