UID:
almahu_9949567209102882
Format:
XI, 467 p. 292 illus., 242 illus. in color.
,
online resource.
Edition:
1st ed. 2023.
ISBN:
9789819935376
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9789819935369
Additional Edition:
Printed edition: ISBN 9789819935383
Additional Edition:
Printed edition: ISBN 9789819935390
Language:
English
DOI:
10.1007/978-981-99-3537-6
URL:
https://doi.org/10.1007/978-981-99-3537-6
URL:
Volltext
(URL des Erstveröffentlichers)
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