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Research on Fault Diagnosis Based on Artificial Neural Network

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Communications, Signal Processing, and Systems (CSPS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 517))

Abstract

According to classification framework of classical neural network, contemporary neural network, and soft computing, the basic concepts of the feedforward neural network (MLP, BP, and RBF), the feedback neural network (Hopfield, Boltzmann, Elman), the self-organizing neural network (SOM, ART, CPN), deep and extreme neural network (Deep learning, extreme learning), the novel neural network (SVM, PNN), and soft computing neural networks combined with various methods are introduced, and the research progress and typical applications of the neural network in fault diagnosis are given. The existing problems and future development directions of fault diagnosis are also discussed.

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Acknowledgements

The 207th Research Institute of NORINCO GROUP, Project 61603006 Supported by NSFC.

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Authors and Affiliations

  1. North Automatic Control Technology Institute, Tiyu Road no. 351, Taiyuan, 030006, China

    Rui Liu

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  1. Rui Liu
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Correspondence to Rui Liu .

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Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Liu, R. (2020). Research on Fault Diagnosis Based on Artificial Neural Network. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_10

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  • DOI: https://doi.org/10.1007/978-981-13-6508-9_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6507-2

  • Online ISBN: 978-981-13-6508-9

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