A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence
The rapid advancement of Industry 4.0 and intelligent manufacturing has elevated the demands for fault diagnosis in servo motors. Traditional diagnostic methods, which rely heavily on handcrafted features and expert knowledge, struggle to achieve efficient fault identification in complex industrial...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/9 |
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| author | Zheng Zhou Yusong Qiao Xusheng Lin Purui Li Nan Wu Dong Yu |
| author_facet | Zheng Zhou Yusong Qiao Xusheng Lin Purui Li Nan Wu Dong Yu |
| author_sort | Zheng Zhou |
| collection | DOAJ |
| description | The rapid advancement of Industry 4.0 and intelligent manufacturing has elevated the demands for fault diagnosis in servo motors. Traditional diagnostic methods, which rely heavily on handcrafted features and expert knowledge, struggle to achieve efficient fault identification in complex industrial environments, particularly when faced with real-time performance and accuracy limitations. This paper proposes a novel fault diagnosis approach integrating multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms to address these challenges. Furthermore, the proposed method is optimized for deployment on resource-constrained edge devices through knowledge distillation and model quantization. This approach significantly reduces the computational complexity of the model while maintaining high diagnostic accuracy, making it well suited for edge nodes in industrial IoT scenarios. Experimental results demonstrate that the method achieves efficient and accurate servo motor fault diagnosis on edge devices with excellent accuracy and inference speed. |
| format | Article |
| id | doaj-art-ebb0ab6847524820882f32d8d60df46d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ebb0ab6847524820882f32d8d60df46d2025-01-10T13:20:32ZengMDPI AGSensors1424-82202024-12-01251910.3390/s25010009A Deployment Method for Motor Fault Diagnosis Application Based on Edge IntelligenceZheng Zhou0Yusong Qiao1Xusheng Lin2Purui Li3Nan Wu4Dong Yu5Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaThe rapid advancement of Industry 4.0 and intelligent manufacturing has elevated the demands for fault diagnosis in servo motors. Traditional diagnostic methods, which rely heavily on handcrafted features and expert knowledge, struggle to achieve efficient fault identification in complex industrial environments, particularly when faced with real-time performance and accuracy limitations. This paper proposes a novel fault diagnosis approach integrating multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms to address these challenges. Furthermore, the proposed method is optimized for deployment on resource-constrained edge devices through knowledge distillation and model quantization. This approach significantly reduces the computational complexity of the model while maintaining high diagnostic accuracy, making it well suited for edge nodes in industrial IoT scenarios. Experimental results demonstrate that the method achieves efficient and accurate servo motor fault diagnosis on edge devices with excellent accuracy and inference speed.https://www.mdpi.com/1424-8220/25/1/9intelligent CNC systemsedge intelligencefault diagnosismodel deployment |
| spellingShingle | Zheng Zhou Yusong Qiao Xusheng Lin Purui Li Nan Wu Dong Yu A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence Sensors intelligent CNC systems edge intelligence fault diagnosis model deployment |
| title | A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence |
| title_full | A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence |
| title_fullStr | A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence |
| title_full_unstemmed | A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence |
| title_short | A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence |
| title_sort | deployment method for motor fault diagnosis application based on edge intelligence |
| topic | intelligent CNC systems edge intelligence fault diagnosis model deployment |
| url | https://www.mdpi.com/1424-8220/25/1/9 |
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