An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion

Xianzhang Zhou; Aohan Li; Guangjie Han

doi:10.3390/s23177567

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion

Sensors (Basel). 2023 Aug 31;23(17):7567. doi: 10.3390/s23177567.

Authors

Xianzhang Zhou¹, Aohan Li², Guangjie Han³

Affiliations

¹ Chongqing Academy of Education Science, Chongqing 400015, China.
² Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 1828585, Japan.
³ Department of Internet of Things Engineering, Hohai University, Changzhou 213022, China.

Abstract

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

Keywords: bearing fault diagnosis; industrial IoT; small sample fusion; transfer learning.

Grants and funding

This work was partially supported by the 2021 key project of Chongqing Education Science “14th Five Year Plan” under grant number 2021-00-285, the 2021 Chongqing Municipal Education Commission Science and Technology Research Plan Major Project under grant number KJZD-M20211440, the 2022 Chongqing Municipal Research Institute Performance Incentive Guidance Special Project under grant number cstc2022jxjl40004, and the Major Scientific Research Project of Chongqing Vocational Education Society in 2022–2023 under grant number 2022ZJXH431003.