Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)

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Signal Processing Technologies and Fault Diagnosis Methods Based on Edge Computing

Authors
Wenyi Guo1, *
1Southampton Ocean Engineering Joint Institute at HEU, Harbin Engineering University, Harbin, Heilongjiang, 150001, China
*Corresponding author. Email: g20040428wy@hrbeu.edu.cn
Corresponding Author
Wenyi Guo
Available Online 23 October 2025.
DOI
10.2991/978-94-6463-864-6_49How to use a DOI?
Keywords
Edge Computing; Industrial Internet Of Things (Iiot); Signal Processing; Fault Diagnosis; Mobilenet
Abstract

With the rapid development of the Industrial Internet of Things (IIoT) and smart manufacturing, machine signal monitoring and fault diagnosis have become critical technologies for ensuring equipment safety and operational efficiency. Traditional cloud computing-based detection methods face limitations in high data transmission volumes, significant bandwidth consumption, and poor real-time performance. Edge computing has garnered increasing attention from researchers due to its advantages in low latency, localized processing, and resource collaboration. This paper discusses the application of edge computing in machine signal processing and fault diagnosis and reviews recent key technologies and applications, with a focus on signal acquisition, preprocessing, feature extraction, and fault diagnosis methods based on lightweight deep learning models. Additionally, it analyzes data compression, real-time transmission techniques, and model deployment strategies in wireless sensor networks for rotating machinery applications. By comparing the latest research advancements, this study identifies future research directions, including edge computing platform design, algorithm lightweighting, and hardware-software co-optimization.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
Series
Advances in Engineering Research
Publication Date
23 October 2025
ISBN
978-94-6463-864-6
ISSN
2352-5401
DOI
10.2991/978-94-6463-864-6_49How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Wenyi Guo
PY  - 2025
DA  - 2025/10/23
TI  - Signal Processing Technologies and Fault Diagnosis Methods Based on Edge Computing
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 551
EP  - 566
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_49
DO  - 10.2991/978-94-6463-864-6_49
ID  - Guo2025
ER  -