Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems / by Yaguo Lei, Naipeng Li, Xiang Li.

Format:

Book

Author/Creator:

Lei, Yaguo, author.

Li, Naipeng, author.

Li, Xiang, author.

Series:

Engineering Series

Language:

English

Subjects (All):

Machinery.

Machinery and Machine Elements.

Local Subjects:

Machinery and Machine Elements.

Physical Description:

1 online resource (292 pages)

Edition:

1st ed. 2023.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2023.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Lei, Yaguo Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

ISBN:

981-16-9131-2

OCLC:

1493008948