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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

Yan, Ruqiang
Zhao, Zhibin

Hardcover

Technology & EngineeringGeneral Computers

ISBN10: 1032752378
ISBN13: 9781032752372
Publisher: CRC Press
Published: Jun 6 2024
Pages: 206
Weight: 1.32
Height: 0.56 Width: 7.00 Depth: 10.00
Language: English

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 revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains.

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