Deep neural networks-enabled intelligent fault diagnosis of mechanical systems / Ruqiang Yan and Zhibin Zhao.

Format:

Book

Author/Creator:

Yan, Ruqiang, author.

Zhao, Zhibin, 1993- author.

Language:

English

Subjects (All):

Fault location (Engineering)--Data processing.

Fault location (Engineering).

Deep learning (Machine learning)--Industrial applications.

Deep learning (Machine learning).

Physical Description:

1 online resource (x, 206 pages) : illustrations

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2024

Summary:

"The book aims to highlight the potential of Deep Learning (DL)-enabled methods in Intelligent Fault Diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionise the nature of IFD, the book contributes to improved efficiency, safety and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning"-- Provided by publisher.

Contents:

Auto-encoders for intelligent fault diagnosis

Deep belief networks for intelligent fault diagnosis

Convolutional neural networks for intelligent fault diagnosis

Data augmentation for intelligent fault diagnosis

Multi-sensor fusion for intelligent fault diagnosis

Unsupervised deep transfer learning for intelligent fault diagnosis

Neural architecture search for intelligent fault diagnosis

Self-supervised learning (SSF) for intelligent fault diagnosis

Reinforcement learning for intelligent fault diagnosis.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781040026618

OCLC:

1449627118