{"product_id":"modeling-remaining-useful-life-dynamics-in-reliability-engineering-9781032168593","title":"Modeling Remaining Useful Life Dynamics in Reliability Engineering","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThis book applies traditional reliability engineering methods to prognostics and health management (PHM), looking at remaining useful life (RUL) and its dynamics, to enable engineers to effectively and accurately predict machinery and systems useful lifespan. It presents an innovative general method for defining a nonlinear transformation enabling the mean residual life to become a linear function of time and applies this method to frequently encountered time-to-failure distributions and degradation processes. Statistical estimation techniques are then presented to estimate RUL from field data, and risk-based methods for maintenance optimization are described, including the use of RUL dynamics for predictive maintenance. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 181 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 06 June 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e This book presents traditional reliability engineering methods for prognostics and health management (PHM), focusing on remaining useful life (RUL) and its dynamics. The RUL indicator is crucial in defining and implementing predictive maintenance policies, but it is essential to account for uncertainty to avoid incorrect decisions. The book explores various methods for estimating RUL, including model-based, data-driven, and hybrid approaches. It introduces an innovative method for defining a nonlinear transformation to make the mean residual life a linear function of time. The author applies this method to common time-to-failure distributions and degradation processes. Recent research results are incorporated, and statistical estimation techniques are presented to estimate RUL from field data. Risk-based methods for maintenance optimization are described, including the use of RUL dynamics for predictive maintenance. The book concludes with suggestions for future research, including links with machine learning and deep learning. Industrial examples illustrate the theory. Each chapter is followed by a summary.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 530g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 229 x 152 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032168593\u003c\/p\u003e","brand":"Pierre Dersin","offers":[{"title":"Hardback","offer_id":44275751289082,"sku":"9781032168593","price":88.52,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1686306386382_book.jpg?v=1686476339","url":"https:\/\/shulphink.com\/products\/modeling-remaining-useful-life-dynamics-in-reliability-engineering-9781032168593","provider":"Shulph Ink","version":"1.0","type":"link"}