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Prognostics and Health Management of Engineering Systems - An Introduction (Paperback, Softcover reprint of the original 1st ed. 2017)
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Prognostics and Health Management of Engineering Systems - An Introduction (Paperback, Softcover reprint of the original 1st ed. 2017)
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This book introduces the methods for predicting the future behavior
of a system's health and the remaining useful life to determine an
appropriate maintenance schedule. The authors introduce the
history, industrial applications, algorithms, and benefits and
challenges of PHM (Prognostics and Health Management) to help
readers understand this highly interdisciplinary engineering
approach that incorporates sensing technologies, physics of
failure, machine learning, modern statistics, and reliability
engineering. It is ideal for beginners because it introduces
various prognostics algorithms and explains their attributes, pros
and cons in terms of model definition, model parameter estimation,
and ability to handle noise and bias in data, allowing readers to
select the appropriate methods for their fields of
application.Among the many topics discussed in-depth are:*
Prognostics tutorials using least-squares* Bayesian inference and
parameter estimation* Physics-based prognostics algorithms
including nonlinear least squares, Bayesian method, and particle
filter* Data-driven prognostics algorithms including Gaussian
process regression and neural network* Comparison of different
prognostics algorithms The authors also present several
applications of prognostics in practical engineering systems,
including wear in a revolute joint, fatigue crack growth in a
panel, prognostics using accelerated life test data, fatigue damage
in bearings, and more. Prognostics tutorials with a Matlab code
using simple examples are provided, along with a companion website
that presents Matlab programs for different algorithms as well as
measurement data. Each chapter contains a comprehensive set of
exercise problems, some of which require Matlab programs, making
this an ideal book for graduate students in mechanical, civil,
aerospace, electrical, and industrial engineering and engineering
mechanics, as well as researchers and maintenance engineers in the
above fields.
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