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Electronic reliability prediction: a study over 25 years

By Jeffrey Alun Jones


This thesis describes research work that the author has undertaken and published in the field of electronic reliability prediction techniques over the last 25 years. Reliability prediction is an important area since it has been part of the backbone of reliability engineering in one form or another for over fifty years.\ud The author has over 45 publications that are within the area of reliability prediction and 13 of these have been selected for review in this thesis. In order to show how the author’s work has contributed to the field of reliability prediction this document also contains information on the history of reliability prediction. This allows the author’s work to be placed in context with general developments in the field.\ud The contributions to knowledge and innovations that have been made in reliability prediction include the development of statistical models for lifetime prediction using early life data (i.e. prognostics); the use of non-constant failure rates for reliability prediction; the use of neural networks for reliability prediction, the use of artificial intelligence systems to support reliability engineers’ decision making; the use of a holistic approach to reliability; the use of complex discrete events simulation to model equipment availability; demonstration of the weaknesses of classical reliability prediction; an understanding of the basic behaviour of no fault founds; the development of a parametric drift model; identification of the use of a reliability database to improve the reliability of systems; and an understanding of the issues that surround the use of new reliability metrics in the aerospace industry

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