Study of Spiking Neural Networks Fault Diagnosis Model for Equipment

Hanyang Wang; Ming Luo; Fengshou Gu

doi:10.1007/978-3-031-73407-6_49

Study of Spiking Neural Networks Fault Diagnosis Model for Equipment

Hanyang Wang^*, Ming Luo, Fengshou Gu

^*Corresponding author for this work

University of Bristol Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Spiking Neural Network (SNN) is a new generation of artificial neural network with more bio-interpretability, which has the advantages of unique information encoding and processing, rich spatiotemporal dynamics, and low-power event-driven working mode. It has received wide attention in recent years for different areas. The fundamental elements and underlying learning algorithms of SNN are comprehensively introduced. The SNNs classical neuronal model, synaptic plasticity mechanism, especially the information encoding methods, learning algorithms are discussed. An analysis is conducted to explore the strengths and weaknesses of diverse models, highlighting their respective advantages and disadvantages. Finally, the Spiking neural network algorithm of fault diagnosis model with effective neuronal model and synaptic plasticity mechanism is presented with the application scenarios and related potentials. It is expected to further improve the accuracy of equipment fault diagnosis and expand the broader application prospects of artificial intelligence.

Original language	English
Title of host publication	Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP
Editors	Zuolu Wang, Kai Zhang, Ke Feng, Yuandong Xu, Wenxian Yang
Publisher	Springer
Pages	541-552
Number of pages	12
ISBN (Electronic)	9783031734076
ISBN (Print)	9783031734069, 9783031734090
DOIs	https://doi.org/10.1007/978-3-031-73407-6_49
Publication status	Published - 19 Oct 2024
Event	TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024 - Qingdao, China Duration: 8 May 2024 → 11 May 2024

Publication series

Name	Mechanisms and Machine Science
Volume	141 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024
Country/Territory	China
City	Qingdao
Period	8/05/24 → 11/05/24

Bibliographical note

Publisher Copyright:
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Encoding Methods
Equipment Fault Diagnosis
Fault Diagnosis Model
Spiking Neural Network
Synaptic Plasticity Mechanism

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10.1007/978-3-031-73407-6_49

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Wang, H., Luo, M., & Gu, F. (2024). Study of Spiking Neural Networks Fault Diagnosis Model for Equipment. In Z. Wang, K. Zhang, K. Feng, Y. Xu, & W. Yang (Eds.), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP (pp. 541-552). (Mechanisms and Machine Science; Vol. 141 MMS). Springer. https://doi.org/10.1007/978-3-031-73407-6_49

@inproceedings{76ff5c9c6741451391f635ebc886d259,

title = "Study of Spiking Neural Networks Fault Diagnosis Model for Equipment",

abstract = "Spiking Neural Network (SNN) is a new generation of artificial neural network with more bio-interpretability, which has the advantages of unique information encoding and processing, rich spatiotemporal dynamics, and low-power event-driven working mode. It has received wide attention in recent years for different areas. The fundamental elements and underlying learning algorithms of SNN are comprehensively introduced. The SNNs classical neuronal model, synaptic plasticity mechanism, especially the information encoding methods, learning algorithms are discussed. An analysis is conducted to explore the strengths and weaknesses of diverse models, highlighting their respective advantages and disadvantages. Finally, the Spiking neural network algorithm of fault diagnosis model with effective neuronal model and synaptic plasticity mechanism is presented with the application scenarios and related potentials. It is expected to further improve the accuracy of equipment fault diagnosis and expand the broader application prospects of artificial intelligence.",

keywords = "Encoding Methods, Equipment Fault Diagnosis, Fault Diagnosis Model, Spiking Neural Network, Synaptic Plasticity Mechanism",

author = "Hanyang Wang and Ming Luo and Fengshou Gu",

note = "Publisher Copyright: {\textcopyright} 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG.; TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024 ; Conference date: 08-05-2024 Through 11-05-2024",

year = "2024",

month = oct,

day = "19",

doi = "10.1007/978-3-031-73407-6\_49",

language = "English",

isbn = "9783031734069",

series = "Mechanisms and Machine Science",

publisher = "Springer",

pages = "541--552",

editor = "Zuolu Wang and Kai Zhang and Ke Feng and Yuandong Xu and Wenxian Yang",

booktitle = "Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP",

address = "United States",

}

Wang, H, Luo, M & Gu, F 2024, Study of Spiking Neural Networks Fault Diagnosis Model for Equipment. in Z Wang, K Zhang, K Feng, Y Xu & W Yang (eds), Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. Mechanisms and Machine Science, vol. 141 MMS, Springer, pp. 541-552, TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024, Qingdao, China, 8/05/24. https://doi.org/10.1007/978-3-031-73407-6_49

Study of Spiking Neural Networks Fault Diagnosis Model for Equipment. / Wang, Hanyang; Luo, Ming; Gu, Fengshou.
Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP. ed. / Zuolu Wang; Kai Zhang; Ke Feng; Yuandong Xu; Wenxian Yang. Springer, 2024. p. 541-552 (Mechanisms and Machine Science; Vol. 141 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Study of Spiking Neural Networks Fault Diagnosis Model for Equipment

AU - Wang, Hanyang

AU - Luo, Ming

AU - Gu, Fengshou

PY - 2024/10/19

Y1 - 2024/10/19

N2 - Spiking Neural Network (SNN) is a new generation of artificial neural network with more bio-interpretability, which has the advantages of unique information encoding and processing, rich spatiotemporal dynamics, and low-power event-driven working mode. It has received wide attention in recent years for different areas. The fundamental elements and underlying learning algorithms of SNN are comprehensively introduced. The SNNs classical neuronal model, synaptic plasticity mechanism, especially the information encoding methods, learning algorithms are discussed. An analysis is conducted to explore the strengths and weaknesses of diverse models, highlighting their respective advantages and disadvantages. Finally, the Spiking neural network algorithm of fault diagnosis model with effective neuronal model and synaptic plasticity mechanism is presented with the application scenarios and related potentials. It is expected to further improve the accuracy of equipment fault diagnosis and expand the broader application prospects of artificial intelligence.

AB - Spiking Neural Network (SNN) is a new generation of artificial neural network with more bio-interpretability, which has the advantages of unique information encoding and processing, rich spatiotemporal dynamics, and low-power event-driven working mode. It has received wide attention in recent years for different areas. The fundamental elements and underlying learning algorithms of SNN are comprehensively introduced. The SNNs classical neuronal model, synaptic plasticity mechanism, especially the information encoding methods, learning algorithms are discussed. An analysis is conducted to explore the strengths and weaknesses of diverse models, highlighting their respective advantages and disadvantages. Finally, the Spiking neural network algorithm of fault diagnosis model with effective neuronal model and synaptic plasticity mechanism is presented with the application scenarios and related potentials. It is expected to further improve the accuracy of equipment fault diagnosis and expand the broader application prospects of artificial intelligence.

KW - Encoding Methods

KW - Equipment Fault Diagnosis

KW - Fault Diagnosis Model

KW - Spiking Neural Network

KW - Synaptic Plasticity Mechanism

U2 - 10.1007/978-3-031-73407-6_49

DO - 10.1007/978-3-031-73407-6_49

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SN - 9783031734069

SN - 9783031734090

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SP - 541

EP - 552

BT - Proceedings of the TEPEN International Workshop on Fault Diagnostic and Prognostic - TEPEN2024-IWFDP

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A2 - Zhang, Kai

A2 - Feng, Ke

A2 - Xu, Yuandong

A2 - Yang, Wenxian

PB - Springer

T2 - TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

Y2 - 8 May 2024 through 11 May 2024

ER -

Study of Spiking Neural Networks Fault Diagnosis Model for Equipment

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