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Big data-driven intelligent fault diagnosis and prognosis for mechanical systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data-driven intelligent fault diagnosis and prognosis for mechanical systemsby Yaguo Lei, Naipeng Li, Xiang Li.
Author:	Lei, Yaguo.
other author:	Li, Naipeng.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xiii, 281 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Fault location (Engineering)
Online resource:	https://doi.org/10.1007/978-981-16-9131-7
ISBN:	9789811691317$q(electronic bk.)

Big data-driven intelligent fault diagnosis and prognosis for mechanical systems
Lei, Yaguo.

Big data-driven intelligent fault diagnosis and prognosis for mechanical systems[electronic resource] /by Yaguo Lei, Naipeng Li, Xiang Li. - Singapore :Springer Nature Singapore :2023. - xiii, 281 p. :ill. (chiefly color), digital ;24 cm.

Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.

This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM) Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.

ISBN: 9789811691317$q(electronic bk.)

Standard No.: 10.1007/978-981-16-9131-7doiSubjects--Topical Terms:

266155
Fault location (Engineering)

LC Class. No.: TA169.6

Dewey Class. No.: 620.004

Big data-driven intelligent fault diagnosis and prognosis for mechanical systems
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505 0 $a Introduction and Background -- Traditional Intelligent Fault Diagnosis -- Hybrid Intelligent Fault Diagnosis Methods -- Deep Learning-Based Intelligent Fault Diagnosis -- Data-Driven RUL Prediction -- Data-Model Fusion RUL Prediction.
520 $a This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM) Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era. Features: Addresses the critical challenges in the field of PHM at present Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis Provides abundant experimental validations and engineering cases of the presented methodologies.
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