Modeling Remaining Useful Life Dynamics In Reliability Engineering
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Modeling Remaining Useful Life Dynamics in Reliability Engineering
"Modeling Remaining Useful Life Dynamics in Reliability Engineering applies traditional reliability engineering methods to Prognostics and Health Management (PHM), looking at Remaining Useful Life (RUL) and predictive maintenance to enable engineers to effectively and safely predict machinery lifespan. One of the key tools used in defining and implementing predictive maintenance policies is the RUL indicator. However, it is essential to account for the uncertainty inherent to the RUL, as otherwise predictive maintenance strategies can be incorrect. This can cause high costs, or alternatively, ineffective predictions. Methods used to estimate RUL are very numerous and diverse, and broadly speaking, fall into three categories: model-based, data-driven, or hybrid, which uses both. The book starts by building on established theory, and applying cutting edge research to it, such as artificial intelligence models and deep learning. It looks at traditional reliability engineering methods through their relation to Prognostics and Health Management (PHM) requirements and presents the concept of RUL loss rate. Following on from this, the book presents a general method for defining a nonlinear transformation enabling the MRL to become a linear function. It also touches on topics such as Weibull distribution, gamma distribution and degradation, along with time-to-failure distributions. Features: Provides both practical and theoretical background of RUL. Describes how the uncertainty of RUL can be related to RUL loss rate. Provides new insights into time-to-failure distributions. Offers tools for predictive maintenance. The book will be of interest to engineers and researchers in reliability engineering, Prognostics and Health Management and industry management"--
Modeling Remaining Useful Life Dynamics in Reliability Engineering
This 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. One of the key tools used in defining and implementing predictive maintenance policies is the RUL indicator. However, it is essential to account for the uncertainty inherent to the RUL, as otherwise predictive maintenance strategies can be incorrect. This can cause high costs or, alternatively, inappropriate decisions. Methods used to estimate RUL are numerous and diverse and, broadly speaking, fall into three categories: model-based, data-driven, or hybrid, which uses both. The author starts by building on established theory and looks at traditional reliability engineering methods through their relation to PHM requirements and presents the concept of RUL loss rate. Following on from this, the author presents an innovative general method for defining a nonlinear transformation enabling the mean residual life to become a linear function of time. He applies this method to frequently encountered time-to-failure distributions, such as Weibull and gamma, and degradation processes. Latest research results, including the author’s (some of which were previously unpublished), are drawn upon and combined with very classical work. 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. The book ends with suggestions for future research, including links with machine learning and deep learning. The theory is illustrated by industrial examples. Each chapter is followed by a series of exercises. FEATURES Provides both practical and theoretical background of RUL Describes how the uncertainty of RUL can be related to RUL loss rate Provides new insights into time-to-failure distributions Offers tools for predictive maintenance This book will be of interest to engineers, researchers and students in reliability engineering, prognostics and health management, and maintenance management.
Advances in Risk-Informed Technologies
Author: Prabhakar V. Varde
language: en
Publisher: Springer Nature
Release Date: 2024-01-07
This book presents the latest research in the areas of development and application of risk-informed and risk-based technologies. The book discusses how advances in computational technologies, availability of accumulated experience and data on design, operations, maintenance and regulations, new insights in human factor modelling and development of new technologies, such as physics-of-failure modelling, prognostics and health management, have paved the way for implementation of risk and reliability tools and methods. The book will be useful for researchers, academicians, and engineers, particularly the field engineers, designers and regulators working on complex engineering systems.