Fault Diagnosis of Centrifugal fan Bearings Based on I-CNN and JMMD in the Context of Sample Imbalance

• Autorzy: Gao Yang , Li Xueyi

Identyfikatory

DOI

10.17531/ein/191459

• Języki publikacji: EN

Abstrakty

EN

Bearing fault diagnosis is an effective technical means to improve the reliability of centrifugal fan bearings. In this paper, a transfer learning-based fault diagnosis method for Centrifugal fan bearings is proposed, utilizing the improved CNN (I-CNN) and Joint Maximum Mean Discrepancy (JMMD) algorithms. The raw vibration signals of bearings are enhanced through fast Fourier transform for feature representation. The enhanced signals are then processed by parallel multi-scale CNNs with an embedded Squeeze-and-Excitation (SE) attention mechanism to extract and focus on key features. Furthermore, the JMMD is introduced as a metric for quantifying the disparity between the source and target domains, thereby mitigating domain shift. In the loss function, weight factors and scaling factors are introduced to increase attention on minority samples and easily confused samples within the imbalanced dataset. The proposed method is validated on the Centrifugal fan bearing dataset from Jiangnan University and the CWRU dataset.

Słowa kluczowe

EN

centrifugal fan bearings; sample imbalance; transfer learning; fault diagnosis

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 4
• Strony: art. no. 191459
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Gao Yang

• College of Mathematical Sciences, Daqing Normal University, China

• https://orcid.org/0000-0003-1952-3955

autor

Li Xueyi

• Department of Mechanical Engineering, Tsinghua University, China
• College of Mechanical and Electrical Engineering, Northeast Forestry University, China

• https://orcid.org/0000-0003-0335-0594

Bibliografia

• 1. A. Fortini, A. Suman, and N. Zanini, "An experimental and numerical study of the solid particle erosion damage in an industrial cement large-sized fan," Engineering failure analysis, vol. 146, p. https://doi.org/10.1016/j.engfailanal.2023.107058
• 2. N. Ouerghemmi, S. B. Salem, K. Bacha, and A. Chaari, "V-belt Fault Detection using Motor Current Signature Analysis in Centrifugal Fan System," in 2021 IEEE 2nd International Conference on Signal, Control and Communication (SCC), 2021: IEEE, pp. 1-6. https://doi.org/10.1109/SCC53769.2021.9768345
• 3. C. Medrea, D. Papageorgiou, H. Bravos, and I. Chicinaş, "Failure analysis of a fan blade holding assemblement, installed on the cooling tower of a power plant," Engineering Failure Analysis, vol. 127, p. https://doi.org/10.1016/j.engfailanal.2021.105505
• 4. M. Kumar, T. Maity, and M. K. Kirar, "Energy savings through VOD (Ventilation-on-Demand) analysis in Indian underground coal mine," IEEE Access, vol. 10, pp. https://doi.org/10.1109/ACCESS.2022.3203710
• 5. Y. Yang et al., "Purification technology of oil mist in industrial buildings: A review," Building and Environment, p. https://doi.org/10.1016/j.buildenv.2023.110229.
• 6. J. Liu and Y. Shao, "Overview of dynamic modelling and analysis of rolling element bearings with localized and distributed faults," Nonlinear Dynamics, vol. 93, no. 4, 2018. https://doi.org/10.1007/s11071-018-4314-y
• 7. Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, and A. K. Nandi, "Applications of machine learning to machine fault diagnosis: A review and roadmap," Mechanical Systems and Signal Processing, vol. 138, p. https://doi.org/10.1016/j.ymssp.2019.106587
• 8. A. Dogan and D. Birant, "Machine learning and data mining in manufacturing," Expert Systems with Applications, vol. 166, p. 114060, 2021. https://doi.org/10.1016/j.eswa.2020.114060
• 9. R. Yan et al., "Wavelet transform for rotary machine fault diagnosis: 10 years revisited," Mechanical Systems and Signal Processing, vol. 200, p. 110545, 2023. https://doi.org/10.1016/j.ymssp.2023.110545
• 10. K. O. M. Touati, M. Boudiaf, L. Mazouz, and L. Cherroun, "Efficient hybrid strategy based on FFT and fuzzy logic techniques applied to fault diagnosis in power transmission line," Soft Computing, pp. 1-20, 2023. https://doi.org/10.1007/s00500-023-09089-6
• 11. Z. Chen, A. Mauricio, W. Li, and K. Gryllias, "A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks," Mechanical Systems and Signal Processing, vol. 140, p. 106683, 2020. https://doi.org/10.1016/j.ymssp.2020.106683
• 12. X. Jiang et al., "An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis," Structural Health Monitoring, vol. 20, no. 5, pp. 2708-2725, 2021. https://doi.org/10.1177/1475921720970856
• 13. A. Abid, M. T. Khan, and J. Iqbal, "A review on fault detection and diagnosis techniques: basics and beyond," Artificial Intelligence Review, vol. 54, no. 5, pp. 3639-3664, 2021. https://doi.org/10.1007/s10462-020-09934-2
• 14. J. Jiao, M. Zhao, J. Lin, and K. Liang, "A comprehensive review on convolutional neural network in machine fault diagnosis," Neurocomputing, vol. 417, pp. 36-63, 2020. https://doi.org/10.1016/j.neucom.2020.07.088
• 15. X. Li, K. Su, D. Li, Q. He, Z. Xie, and X. Kong, "Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method," Measurement Science and Technology, vol. 35, no. 4, p. 046106, 2024. https://doi.org/10.1088/1361-6501/ad19c2
• 16. H. Tang et al., "Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter," Engineering Applications of Artificial Intelligence, vol. 127, p. 107138, 2024. https://doi.org/10.1016/j.engappai.2023.107138
• 17. L. Xie, Q. Miao, Y. Chen, W. Liang, and M. Pecht, "Fan bearing fault diagnosis based on continuous wavelet transform and autocorrelation," in Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 2012: IEEE, pp. 1-6. https://doi.org/10.1109/PHM.2012.6228837
• 18. W. Cui et al., "Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions," Entropy, vol. 25, no. 8, p. 1233, 2023. https://doi.org/10.3390/e25081233
• 19. W. He, Q. Miao, M. Azarian, and M. Pecht, "Health monitoring of cooling fan bearings based on wavelet filter," Mechanical Systems and Signal Processing, vol. 64, pp. 149-161, 2015. https://doi.org/10.1016/j.ymssp.2015.04.002
• 20. C. Qian, J. Zhu, Y. Shen, Q. Jiang, and Q. Zhang, "Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge," Neural Processing Letters, vol. 54, no. 3, pp. 2509-2531, 2022. https://doi.org/10.1007/s11063-021-10719-z
• 21. X. Zhao, M. Ma, and F. Shao, "Bearing fault diagnosis method based on improved Siamese neural network with small sample," Journal of Cloud Computing, vol. 11, no. 1, p. 79, 2022. https://doi.org/10.1186/s13677-022-00350-1
• 22. H. Cao, H. Shao, X. Zhong, Q. Deng, X. Yang, and J. Xuan, "Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds," Journal of Manufacturing Systems, vol. 62, pp. 186-198, 2022. https://doi.org/10.1016/j.jmsy.2021.11.016
• 23. Y. Xiao, H. Shao, Z. Min, H. Cao, X. Chen, and J. J. Lin, "Multiscale dilated convolutional subdomain adaptation network with attention for unsupervised fault diagnosis of rotating machinery cross operating conditions," Measurement, vol. 204, p. 112146, 2022. https://doi.org/10.1016/j.measurement.2022.112146
• 24. W. Mao, Y. Liu, L. Ding, and Y. Li, "Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A comparative study," Ieee Access, vol. 7, pp. 9515-9530, 2019. https://doi.org/10.1109/ACCESS.2018.2890693
• 25. Q. Hang, J. Yang, and L. Xing, "Diagnosis of rolling bearing based on classification for high dimensional unbalanced data," IEEE Access, vol. 7, pp. 79159-79172, 2019. https://doi.org/10.1109/ACCESS.2019.2919406
• 26. J. Lu, W. Wu, X. Huang, Q. Yin, K. Yang, and S. Li, "A modified active learning intelligent fault diagnosis method for rolling bearings with unbalanced samples," Advanced Engineering Informatics, vol. 60, p. 102397, 2024. https://doi.org/10.1016/j.aei.2024.102397
• 27. V. Rai and A. Mohanty, "Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform," Mechanical systems and signal processing, vol. 21, no. 6, pp. 2607-2615, 2007. https://doi.org/10.1016/j.ymssp.2006.12.004
• 28. G. Mesnil et al., "Unsupervised and transfer learning challenge: a deep learning approach," in Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 2012: JMLR Workshop and Conference Proceedings, pp. 97-110.
• 29. Z. Zhao et al., "Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-28, 2021. https://doi.org/10.1109/TIM.2021.3116309
• 30. R. K. Sanodiya and L. Yao, "Unsupervised transfer learning via relative distance comparisons," IEEE Access, vol. 8, pp. 110290-110305, 2020. https://doi.org/10.1109/ACCESS.2020.3002666
• 31. X. Li, P. Yuan, X. Wang, D. Li, Z. Xie, and X. Kong, "An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution," Measurement Science and Technology, vol. 34, no. 9, p. 095401, 2023. https://doi.org/10.1088/1361-6501/acda55
• 32. C. Che, H. Wang, Q. Fu, and X. Ni, "Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions," Advances in Mechanical Engineering, vol. 11, no. 12, p. 1687814019897212, 2019. https://doi.org/10.1177/1687814019897212
• 33. Z. Liu et al., "Swin transformer: Hierarchical vision transformer using shifted windows," in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10012-10022. https://doi.org/10.1109/ICCV48922.2021.00986
• 34. K. Li, L. Su, J. Wu, H. Wang, and P. Chen, "A rolling bearing fault diagnosis method based on variational mode decomposition and an improved kernel extreme learning machine," Applied Sciences, vol. 7, no. 10, p. 1004, 2017. https://doi.org/10.3390/app7101004