Research on a Lightweight Multi-Scale Feature Fusion and its Fault Diagnosis Method for Rolling Bearing with Limited Labeled Samples

• Autorzy: Zhang Jiqiang , Kong Xiangwei , Han Taorui , Cheng Liu , Li Xueyi , Liu Zhitong

Identyfikatory

DOI

10.17531/ein/192235

• Języki publikacji: EN

Abstrakty

EN

Convolutional neural networks(CNNs) show significant potential for bearing fault diagnosis. However, traditional CNNs face challenges such as poor noise resistance, high computational complexity, reliance on extensive samples, and limited generalizability. As a result, this paper proposes WDSC-Net, a lightweight, multiscale feature fusion method, focusing on limited labeled fault samples. Initially, a wide kernel convolutional is employed, aiming to reduce parameters and computational complexity. Next, features are fed into a 1×1 convolutional layer reduces feature dimensionality. Subsequently, leveraging the benefits of depth-separable convolution (DSC) allows the separation of spatial and channel features, constructing four convolutional layers of varying scales to amplify the nonlinear fault representation. Finally, an improved feature soft-threshold denoising module is introduced for global feature denoising. Validation on CWRU and MCDS datasets shows that the WDSC-Net method exhibits superior generalizability and noise resistance compared to typical deep-learning fault methods.

Słowa kluczowe

EN

rolling bearing; limited labeled samples; multi-scale feature fusion; depth-separable convolution network; soft threshold

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2025
• Tom: Vol. 27, no. 1
• Strony: art. no. 192235
• Opis fizyczny: Bibliogr. 49 poz., rys., tab., wykr.

Twórcy

autor

Zhang Jiqiang

• School of Mechanical Engineering and Automation, Northeastern University, China

• https://orcid.org/0000-0002-2856-0929

autor

Kong Xiangwei

• School of Mechanical Engineering and Automation, Northeastern University, China
• Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China
• Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment, Northeastern University, Shenyang 110819, China

• https://orcid.org/0000-0001-7042-4368

autor

Han Taorui

• School of Mechanical Engineering and Automation, Northeastern University, China

• https://orcid.org/0000-0001-7966-1677

autor

Cheng Liu

• School of Mechanical Engineering and Automation, Northeastern University, China

• https://orcid.org/0000-0002-7094-3835

autor

Li Xueyi

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

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

autor

Liu Zhitong

• School of Mechanical Engineering and Automation, Northeastern University, China

• https://orcid.org/0009-0002-9318-5376

Bibliografia

• 1. Zhou H, Huang X, Wen G et al. Construction of health indicators for condition monitoring of rotating machinery: A review of the research. Expert systems with applications 2022 203 117297, https://doi.org/10.1016/j.eswa.2022.117297.
• 2. Li Z, Chen J, Zi Y et al. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive. Mechanical systems and signal processing 2017 85 512-529, https://doi.org/10.1016/j.ymssp.2016.08.042.
• 3. Zhang J, Kong X, Cheng L et al. Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism. Eksploatacja i Niezawodność, 2023, 25(1), http://doi.org/10.17531/ein.2023.1.16.
• 4. Kapoor R, Gupta R, Jha S et al. Boosting performance of power quality event identification with KL Divergence measure and standard deviation. Measurement 2018 126 134-142, https://doi.org/10.1016/j.measurement.2018.05.053.
• 5. Zhang Y, Xing K, Bai R et al. An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement 2020 157 107667, https://doi.org/10.1016/j.measurement.2020.107667.
• 6. Zhao R, Yan R, Chen Z et al. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019, 115: 213-237, https://doi.org/10.1016/j.ymssp.2018.05.050.
• 7. Gong W, Wang Y, Zhang M et al. A fast anomaly diagnosis approach based on modified CNN and multisensor data fusion. IEEE Transactions on Industrial Electronics 2021 69(12) 13636-13646, https://doi.org/10.1109/TIE.2021.3135520.
• 8. Xu G, Liu M, Jiang Z et al. Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors 2019 19(5) 1088, https://doi.org/10.3390/s19051088.
• 9. Fuan W, Hongkai J, Haidong S et al. An adaptive deep convolutional neural network for rolling bearing fault diagnosis. Measurement Science and Technology 2017 28(9) 095005, https://doi.org/10.1088/1361-6501/aa6e22.
• 10. Yang J, Zhang Y, Zhu Y. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing 2007 21(5) 2012-2024, https://doi.org/10.1016/j.ymssp.2006.10.005.
• 11. Ye Z, Yu J. Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis. Mechanical Systems and Signal Processing 2021 161 107984, https://doi.org/10.1016/j.ymssp.2021.107984.
• 12. Ke H, Chen D, Li X et al. Towards brain big data classification: Epileptic EEG identification with a lightweight VGGNet on global MIC. IEEE Access 2018 6 14722-14733, https://doi.org/10.1109/ACCESS.2018.2810882.
• 13. Lu S, Lu Z, Zhang Y D. Pathological brain detection based on AlexNet and transfer learning. Journal of computational science 2019 30 41-47, https://doi.org/10.1016/j.jocs.2018.11.008.
• 14. Wu Z, Shen C, Van Den Hengel A. Wider or deeper: Revisiting the resnet model for visual recognition. Pattern recognition 2019 90 119-133, https://doi.org/10.1016/j.patcog.2019.01.006.
• 15. Fang H, Deng J, Zhao B et al. LEFE-net: A lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement 2021 70: 1–11, https://doi.org/10.1109/TIM.2021.3067187.
• 16. Deng J, Jiang W, Zhang Y et al. HS-KDNet: A lightweight network based on hierarchical-split block and knowledge distillation for fault diagnosis with extremely imbalanced data. IEEE Transactions on Instrumentation and Measurement 2021 70: 1–9, https://doi.org/10.1109/TIM.2021.3091498.
• 17. Lu R, Liu S, Gong Z et al. Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis. Sensors 2024 24(6): 1758, https://doi.org/10.3390/s24061758.
• 18. Xiong S, Zhou H, He S et al. Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure. Measurement Science and Technology 2021 32: 085106, https://doi.org/10.1088/1361-6501/abe448
• 19. Liu W, Guo P, Ye L. A low-delay lightweight recurrent neural network (LLRNN) for rotating machinery fault diagnosis. Sensors 2019 19: 3109, https://doi.org/10.3390/s19143109.
• 20. Cui J, Zhong Q, Zheng S et al. A lightweight model for bearing fault diagnosis based on gramian angular field and coordinate attention. Machines 2022 10: 282, https://doi.org/10.3390/machines10040282.
• 21. Qin Z, Li Z, Zhang Z, et al. ThunderNet: Towards real-time generic object detection on mobile devices. Proceedings of the IEEE/CVF international conference on computer vision. 2019 6718-6727. https://doi.org/10.1109/ICCV.2019.00682
• 22. Iandola FN, Han S, Moskewicz MW et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv 2016 https://doi.org/10.48550/arXiv.1602.07360.
• 23. Li Z Q. Adaptive Anti-noise Fault Diagnosis Algorithm for Bearings based on An Improved Convolution Neural Network. Machinery Design & Manufacture 2024 1-7. https://doi.org/10.19356/j.cnki.1001-3997.20240321.015.
• 24. Dong R, Xu Y W, Long Z H et al. Fault Diagnosis Model of Noise-resistant Bearings Using Large Kernel Attention Mechanism. Noise and Vibration Control, 2023 43(02): 162-168, https://doi.org/10.3969/j.issn.1006-1355.2023.02.024.
• 25. Li R, Wu J, Li Y et al. Periodnet: Noise-robust fault diagnosis method under varying speed conditions. IEEE Transactions on Neural Networks and Learning Systems 2023 https://doi.org/ 10.1109/TNNLS.2023.3274290
• 26. Fan W, Chen Z, Li Y et al. A reinforced noise resistant correlation method for bearing condition monitoring. IEEE Transactions on Automation Science and Engineering 2022 20(2): 995-1006, https://doi.org/10.1109/TASE.2022.3177010.
• 27. Dong J, Jiang H, Su D et al. Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment. Measurement Science and Technology, 2024 35(4): 046006, https://doi.org/10.1088/1361-6501/ad2052.
• 28. Yang G, Liu L, Xi C. Bearing fault diagnosis based on sa-acgan data generation model. China Mechanical Engineering 2022 33(13) 1613, https://doi.org/10.3969/j.issn.1004-132X.2022.13.012.
• 29. Meng A, Chen S, Ou Z et al. A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network. Energy 2022 261: 125276, https://doi.org/10.1016/j.energy.2022.125276.
• 30. Lei Y, Yang B, Jiang X et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing 2020 138 106587, https://doi.org/10.1016/j.ymssp.2019.106587.
• 31. Meng X, Hu T, Li J et al. A digital twin library of mechanical transmission system for the application of small sample fault diagnosis problem. Measurement Science and Technology 2024 35(6): 066125, https://doi.org/10.1088/1361-6501/ad34ef.
• 32. Yang J, Liu J, Xie J et al. Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples. IEEE Transactions on Instrumentation and Measurement 2021 70: 1-12, https://doi.org/10.1109/TIM.2021.3119135.
• 33. Zhou X K, Yu J B. Gearbox Fault Diagnosis Based on One-dimension Residual Convolutional Auto-encoder. Journal of Mechanical Engineering 2020 56(7), 96-108. (in Chinese). https://doi.org/10.3901/JME.2020.07.096
• 34. Wang H, Liu Z, Peng D et al. Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis. IEEE Transactions on Industrial Informatics 2019 16(9) 5735-5745, https://doi.org/10.1109/TII.2019.2955540.
• 35. He Z, Yang L, Angizi S et al. Sparse BD-Net: A multiplication-less DNN with sparse binarized depth-wise separable convolution. ACM Journal on Emerging Technologies in Computing Systems (JETC) 2020 16(2) 1-24, https://doi.org/10.1145/3369391.
• 36. Zhang J, Kong X, Cheng L et al. Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism. Eksploatacja i Niezawodność 2023 25(1), http://doi.org/10.17531/ein.2023.1.16.
• 37. Zhang J, Xiangwei K, Xueyi L I et al. Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout. Chinese Journal of Aeronautics 2022 35(10) 301-312, https://doi.org/10.1016/j.cja.2022.03.007.
• 38. Xu X, Wang J, Zhong B et al. Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism. Measurement 2021 177 109254, https://doi.org/10.1016/j.measurement.2021.109254.
• 39. Sun Y, Weng Y, Luo B et al. Gesture recognition algorithm based on multi-scale feature fusion in RGB-D images. IET Image Processing 2023 17(4) 1280-1290, https://doi.org/10.1049/ipr2.12712.
• 40. Guo J, Si Z, Xiang J. A compound fault diagnosis method of rolling bearing based on wavelet scattering transform and improved soft threshold denoising algorithm. Measurement 2022 196: 111276, https://doi.org/10.1016/j.measurement.2022.111276.
• 41. Zhao M, Zhong S, Fu X et al. Deep residual shrinkage networks for fault diagnosis. IEEE Transactions on Industrial Informatics 2019 16(7) 4681-4690, https://doi.org/ 10.1109/TII.2019.2943898.
• 42. Loparo, K A. A bearing data center. Retrieved from http://www.eecs.case.edu/laboratory/bearing/welcome_overview.htm.
• 43. Li X, Zhang W. Deep learning-based partial domain adaptation method on intelligent machinery fault diagnostics. IEEE Transactions on Industrial Electronics 2020 68(5) 4351-4361 https://doi.org/10.1109/TIE.2020.2984968.
• 44. Sun L, Zhu X, Xiao J et al. A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions. Measurement Science and Technology 2024 35(7): 076201, https://doi.org/10.1088/1361-6501/ad3669.
• 45. Zhang W, Peng G, Li C et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 2017 17(2) 425, https://doi.org/10.3390/s17020425.
• 46. Zhu Y, Li G, Wang R et al. Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization. Applied Acoustics 2021 183: 108336, https://doi.org/10.1016/j.apacoust.2021.108336.
• 47. Xu H, Wamg Z. Condition Evaluation and Fault Diagnosis of Power Transformer Based on GAN-CNN. Journal of Electrotechnology, Electrical Engineering and Management 2023 6(3) 8-16, https://doi.org/10.23977/jeeem.2023.060302.
• 48. Hu Z, Han T, Bian J et al. A deep feature extraction approach for bearing fault diagnosis based on multi-scale convolutional autoencoder and generative adversarial networks. Measurement Science and Technology 2022 33(6) 065013, https://doi.org/10.1088/1361-6501/ac56f0.
• 49. Wu X, Zhang Y, Cheng C et al. A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery. Mechanical Systems and Signal Processing 2021 149 107327, https://doi.org/10.1016/j.ymssp.2020.107327.