Noise-Boosted Convolutional Neural Network for Edge-based Motor Fault Diagnosis with Limited Samples

Chen, Lv and An, Kang and Huang, Dali and Wang, Xiaoxian and Xia, Min and Lu, Siliang (2022) Noise-Boosted Convolutional Neural Network for Edge-based Motor Fault Diagnosis with Limited Samples. IEEE Transactions on Industrial Informatics. ISSN 1551-3203

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Abstract

Convolutional neural networks (CNNs) have been widely applied in motor fault diagnosis. However, to obtain high recognition accuracy, massive training data are typically required and transmitted to the cloud/local server for training, which may suffer from security and privacy problems. In this study, a noise-boosted CNN (NBCNN) model is developed to achieve accelerated training and improved recognition accuracy with limited training samples. First, the NBCNN model with a noise-injection fully connected layer is established. Then, a strategy for noise selection and injection is proposed to obtain an optimal matching among the data, model, and noise. Finally, the optimal injected noise accelerates the convergence of model training and improves the accuracy of motor fault diagnosis. Compared with the conventional CNN without noise injection and the state-of-the-art models, the effectiveness and superiority of the proposed NBCNN model are validated by two benchmark datasets. In addition, the algorithm is deployed onto an edge device and the results show that the training speed of the developed NBCNN can reach nine times faster than the conventional CNN. The proposed method shows remarkable potential for distributed model training, federal learning, and real-time motor fault diagnosis.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Industrial Informatics
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
184739
Deposited By:
Deposited On:
23 Jan 2023 12:50
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Jan 2023 02:07