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Intelligent fault diagnosis and heal...

Li, Weihua.

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Intelligent fault diagnosis and health assessment for complex electro-mechanical systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent fault diagnosis and health assessment for complex electro-mechanical systems/ by Weihua Li, Xiaoli Zhang, Ruqiang Yan.
Author:	Li, Weihua.
other author:	Zhang, Xiaoli.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xi, 467 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Supervised SVM based intelligent fault diagnosis methods -- Chapter 3 Semi-supervised Learning Based Intelligent Fault Diagnosis Methods -- Chapter 4 Manifold learning based intelligent fault diagnosis and prognostics -- Chapter 5 Deep learning based machinery fault diagnosis -- Chapter 6 Phase space reconstruction based on machinery system degradation tracking and fault prognostics -- Chapter 7 Complex electro-mechanical system operational reliability assessment and health maintenance.
Contained By:	Springer Nature eBook
Subject:	Fault location (Engineering) -
Online resource:	https://doi.org/10.1007/978-981-99-3537-6
ISBN:	9789819935376

Intelligent fault diagnosis and health assessment for complex electro-mechanical systems
Li, Weihua.

Intelligent fault diagnosis and health assessment for complex electro-mechanical systems[electronic resource] /by Weihua Li, Xiaoli Zhang, Ruqiang Yan. - Singapore :Springer Nature Singapore :2023. - xi, 467 p. :ill. (some col.), digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Supervised SVM based intelligent fault diagnosis methods -- Chapter 3 Semi-supervised Learning Based Intelligent Fault Diagnosis Methods -- Chapter 4 Manifold learning based intelligent fault diagnosis and prognostics -- Chapter 5 Deep learning based machinery fault diagnosis -- Chapter 6 Phase space reconstruction based on machinery system degradation tracking and fault prognostics -- Chapter 7 Complex electro-mechanical system operational reliability assessment and health maintenance.

Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.

ISBN: 9789819935376

Standard No.: 10.1007/978-981-99-3537-6doiSubjects--Topical Terms:

649702
Fault location (Engineering)

LC Class. No.: TA169.6 / .L59 2023

Dewey Class. No.: 620.00452

Intelligent fault diagnosis and health assessment for complex electro-mechanical systems
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300 $a xi, 467 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1 Introduction -- Chapter 2 Supervised SVM based intelligent fault diagnosis methods -- Chapter 3 Semi-supervised Learning Based Intelligent Fault Diagnosis Methods -- Chapter 4 Manifold learning based intelligent fault diagnosis and prognostics -- Chapter 5 Deep learning based machinery fault diagnosis -- Chapter 6 Phase space reconstruction based on machinery system degradation tracking and fault prognostics -- Chapter 7 Complex electro-mechanical system operational reliability assessment and health maintenance.
520 $a Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
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700 1 $a Yan, Ruqiang. $3 1531766
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