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Modeling Remaining Useful Life Dynamics in Reliability Engineering (Hardcover)
Loot Price: R2,429
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Modeling Remaining Useful Life Dynamics in Reliability Engineering (Hardcover)
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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.
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