Bearing Fault Diagnosis of WDCNN-LSTM in Siamese Network

69d3306d-4e40-4800-995d-3d5a8167b04a20230803062847552wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON COMPUTERS2224-28721109-275010.37394/23205http://wseas.org/wseas/cms.action?id=40261920231920232210.37394/23205.2023.22https://wseas.com/journals/computers/2023.phpBearing Fault Diagnosis of WDCNN-LSTM in Siamese NetworkDaehwanLeeDept. Smart Factory Convergence, Sungkyunkwan University Seoul, REPUBLIC OF KOREAJongpilJeongDept. Smart Factory Convergence, Sungkyunkwan University Seoul, REPUBLIC OF KOREAChaegyuLeeDept. Smart Factory Convergence, Sungkyunkwan University Seoul, REPUBLIC OF KOREAHakjunMoonDept. Computer Science and Engineering, Sungkyunkwan University Seoul, REPUBLIC OF KOREAJaeukLeeDept. Mechanical Engineering Sungkyunkwan University Seoul, REPUBLIC OF KOREADongyoungLeeDept. Mechanical Engineering Sungkyunkwan University Seoul, REPUBLIC OF KOREAIn this paper, a Siamese network-based WDCNN + LSTM model was used to diagnose bearing faults using a few shot learning algorithm. Recently, deep learning-based fault diagnosis methods have achieved good results in equipment fault diagnosis. However, there are still limitations in the existing research. The biggest problem is that a large number of training samples are required to train a deep learning model. However, manufacturing sites are complex, and it is not easy to intentionally create equipment defects. Furthermore, it is impossible to obtain enough training samples for all failure types under all working conditions. Therefore, in this study, we propose a few-shot learning algorithm that can effectively learn with limited data. A Few shot learning algorithm and Siamese network based WDCNN + LSTM model bearing fault diagnosis, which can effectively learn with limited data, is proposed in this study.832023832023848910https://wseas.com/journals/computers/2023/a225105-009(2023).pdf10.37394/23205.2023.22.10https://wseas.com/journals/computers/2023/a225105-009(2023).pdf10.1177/16878132221135745Zhu. Y, chen. H, Meng. W, Xiong. Q and Li. Y, ”A wide kernel CNN-LSTM-based transfer learning method with domain adaptability Fig. 7. Samples 200 Confusion Matrix. Fig. 8. Samples 3000 Confusion Matrix. for rolling bearing fault diagnosis with a small dataset,” Advances in Mechanical Engineering, vol. 14, pp. 1-20, November 2022. 10.3390/electronics11091504Lee. S and Jeong. J, “ SSA-SL Transformer for Bearing Fault Diagnosis under Noisy Factory Environments,” Journal of electronics, vol. 11, pp. 1-21, May 2022. 10.1016/j.ymssp.2006.12.007Widodo. A and Yang. B. 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