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Prognostics and health management fo...

Liu, Hui.

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Prognostics and health management for intelligent electromechanical systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prognostics and health management for intelligent electromechanical systems/ by Hui Liu, Fang Cheng, Yanfei Li.
Author:	Liu, Hui.
other author:	Cheng, Fang.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxi, 208 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Introduction -- Feature extraction of bearing vibration signal -- Ensemble intelligent diagnosis for bearing faults -- Deep learning based prediction for bearing remaining useful life.-Optimization based prediction for IGBT remaining useful life.
Contained By:	Springer Nature eBook
Subject:	Electromechanical devices. -
Online resource:	https://doi.org/10.1007/978-981-96-7218-9
ISBN:	9789819672189

Prognostics and health management for intelligent electromechanical systems
Liu, Hui.

Prognostics and health management for intelligent electromechanical systems[electronic resource] /by Hui Liu, Fang Cheng, Yanfei Li. - Singapore :Springer Nature Singapore :2025. - xxi, 208 p. :ill. (chiefly color), digital ;24 cm.

Introduction -- Feature extraction of bearing vibration signal -- Ensemble intelligent diagnosis for bearing faults -- Deep learning based prediction for bearing remaining useful life.-Optimization based prediction for IGBT remaining useful life.

This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future.

ISBN: 9789819672189

Standard No.: 10.1007/978-981-96-7218-9doiSubjects--Topical Terms:

909355
Electromechanical devices.

LC Class. No.: TK153

Dewey Class. No.: 621.3

Prognostics and health management for intelligent electromechanical systems
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520 $a This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future.
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