Edge Solution for Real-time Motor Fault Diagnosis Based on Efficient Convolutional Neural Network

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: An, Kang, Lu, Jingfeng, Zhu, Quanjing, Wang, Xiaoxian, De Silva, Clarence W., Xia, Min, Lu, Siliang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Cloud computing; Computational modeling; Convolutional neural networks; deep learning; Dynamic control; ECNN; Edge computing; Fault detection; Fault diagnosis; Feature extraction; Machine learning; Model accuracy; motor fault diagnosis; Neural networks; Predictive models; Real time; Real-time systems; Vibrations
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3276513

Real-time motor fault diagnosis can detect motor faults on time and prompt the repair or replacement of faulty motors which minimizes the potential losses caused by motor faults. Deep learning (DL) methods have been intensively applied in motor fault diagnosis. Most DL algorithms need to be trained with sufficient computation resources on cloud or local servers. However, uploading the raw data and downloading the command instructions to the edge will cause inevitable time delays and security concerns. This paper develops a DL algorithm based on efficient convolutional neural networks (ECNN) that can be deployed on an edge computing node for real-time motor fault diagnosis and dynamic control. The effectiveness, efficiency, and robustness of the ECNN model have been validated by experiments, and the results indicate that the ECNN model can achieve 100 % accuracy in recognition of 10 types of motor conditions, with the inference time and memory usage less than 14 ms and 44 KiB, respectively. The comparison results demonstrate that the ECNN model yields higher accuracy than the classical shallow neural networks, and it also presents the advantages of smaller model volume, lower prediction time, and higher accuracy as compared with the DL models. The proposed method shows significant potential for practical application in real-time motor fault detection and control.