Correlation-Enhanced Multi-Scale Residual Network for Bearing Fault Diagnosis in Noisy and Cross-Working Conditions
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Published
Aug 6, 2025
Panfeng Bao
Yue Zhu
Yufeng Shen
Jiashun Ou
Xuening Hu
Abstract
Bearing fault diagnosis under noisy and cross-working conditions remains a challenging task due to complex signal variations and interference. To address this challenge, this paper proposes a Correlation-Enhanced Multi-Scale Residual Network (CE-MSRN), which effectively captures multi-scale fault features while enhancing correlation across different bearing faults. Our model integrates a residual learning framework with a multi-scale feature fusion mechanism, improving robustness against noise and generalization across diverse working conditions. Experimental evaluations on benchmark datasets demonstrate that CE-MSRN achieves superior diagnostic accuracy compared to mainstream methods, exhibiting strong adaptability to unseen fault patterns. These results confirm the potential of our approach for real-time and reliable bearing fault diagnosis in aero-engines and transmission systems.
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Keywords
Bearing fault diagnosis, Multi-scale fusion, Residual network, Complex working condition, Realtime monitoring
References
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Case western reserve university bearing fault data, available: https://engineering.case.edu/bearingdatacenter/downloaddata-file. (2019, December). doi: https://engineering.case.edu/bearingdatacenter/downloaddata-file
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Chen, X., Zhang, B., & Gao, D. (2021, April). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987. doi: 10.1007/s10845-020-01600-2
Dong, Y., Jiang, H., Yao, R., Mu, M., & Yang, Q. (2024, March). Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning. Reliability Engineering & System Safety, 243, 109805. doi: 10.1016/j.ress.2023.109805
Fernández-Francos, D., Martínez-Rego, D., Fontenla-Romero, O., & Alonso-Betanzos, A. (2013, January). Automatic bearing fault diagnosis based on one-class ν-svm. Computers & Industrial Engineering, 64(1), 357–365. doi: 10.1016/j.cie.2012.10.013
He, K., Zhang, X., Ren, S., & Sun, J. (2016, June). Deep residual learning for image recognition. In 2016 ieee conference on computer vision and pattern recognition (cvpr) (pp. 770–778). Las Vegas, NV, USA: IEEE. doi: 10.1109/CVPR.2016.90
Hou, Z.-G., Wang, H.-W., Lv, S.-L., Xiong, M.-L., & Peng, K. (2022, December). Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition. Measurement Science and Technology, 34(3), 035109. doi: 10.1088/1361-6501/aca044
Huo, C., Jiang, Q., Shen, Y., Zhu, Q., & Zhang, Q. (2023, May). Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network. Engineering Applications of Artificial Intelligence, 121, 105970. doi: 10.1016/j.engappai.2023.105970
Kabla, A., & Mokrani, K. (2016). Bearing fault diagnosis using hilbert-huang transform (hht) and support vector machine (svm). Mechanics & Industry, 17(3), 308. doi: 10.1051/meca/2015067
Kingma, D. P., & Ba, J. (2017, January). Adam: A Method for Stochastic Optimization (No. arXiv:1412.6980). arXiv. doi: 10.48550/arXiv.1412.6980
Lai, Z., Yang, C., Lan, S., Wang, L., Shen, W., & Zhu, L. (2024, June). BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning. International Journal of Production Economics, 109319. doi: 10.1016/j.ijpe.2024.109319
Lei, Z., Zhang, P., Chen, Y., Feng, K., Wen, G., Liu, Z., . . . Yang, C. (2023, October). Prior knowledge-embedded meta transfer learning for few-shot fault diagnosis under variable operating conditions. Mechanical Systems and Signal Processing, 200, 110491. doi: 10.1016/j.ymssp.2023.110491
Li, X., Wang, Y., Yao, J., Li, M., & Gao, Z. (2024, May). Multi-sensor fusion fault diagnosis method of wind turbine bearing based on adaptive convergent viewable neural networks. Reliability Engineering & System Safety, 245, 109980. doi: 10.1016/j.ress.2024.109980
Lu, T., Yu, F., Han, B., & Wang, J. (2020). A generic intelligent bearing fault diagnosis system using convolutional neural networks with transfer learning. IEEE Access, 8, 164807–164814. doi: 10.1109/ACCESS.2020.3022840
Luo, M., Li, C., Zhang, X., Li, R., & An, X. (2016, November). Compound feature selection and parameter optimization of elm for fault diagnosis of rolling element bearings. ISA Transactions, 65, 556–566. doi: 10.1016/j.isatra.2016.08.022
Miao, M., Yu, J., & Zhao, Z. (2022, March). A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions. Reliability Engineering & System Safety, 219, 108259. doi: 10.1016/j.ress.2021.108259
Ni, Q., Ji, J. C., Feng, K., Zhang, Y., Lin, D., & Zheng, J. (2024, February). Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit. Reliability Engineering & System Safety, 242, 109753. doi: 10.1016/j.ress.2023.109753
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems (Vol. 32). Curran Associates, Inc.
Peng, D., Wang, H., Desmet, W., & Gryllias, K. (2023, April). RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability. Journal of Dynamics, Monitoring and Diagnostics. doi: 10.37965/jdmd.2023.156
Peng, H., Du, J., Gao, J.,Wang, Y., &Wang,W. (2024, May). Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis. Measurement Science and Technology, 35(5), 056204. doi: 10.1088/1361-6501/ad2828
Peng, H., Wang, W., Gao, J., Wang, Y., & Du, J. (2025). A Lightweight Triple-Stream Network With Multisensor Fusion for Enhanced Few-Shot Learning Fault Diagnosis. IEEE Transactions on Reliability, 1–14. doi: 10.1109/TR.2025.3540500
Qian, Q., Qin, Y., Luo, J., Wang, Y., & Wu, F. (2023, March). Deep discriminative transfer learning network for cross-machine fault diagnosis. Mechanical Systems and Signal Processing, 186, 109884. doi: 10.1016/j.ymssp.2022.109884
Rai, V. K., & Mohanty, A. R. (2007, August). Bearing fault diagnosis using fft of intrinsic mode functions in hilbert–huang transform. Mechanical Systems and Signal Processing, 21(6), 2607–2615. doi: 10.1016/j.ymssp.2006.12.004
Shao, X., & Kim, C.-S. (2024, February). Adaptive multiscale attention convolution neural network for crossdomain fault diagnosis. Expert Systems with Applications, 236, 121216. doi: 10.1016/j.eswa.2023.121216
Tang, H., Tang, Y., Su, Y., Feng, W., Wang, B., Chen, P., & Zuo, D. (2024, January). Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter. Engineering Applications of Artificial Intelligence, 127, 107138. doi: 10.1016/j.engappai.2023.107138
Wan, L., Li, Y., Chen, K., Gong, K., & Li, C. (2022, March). A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis. Measurement, 191, 110752. doi: 10.1016/j.measurement.2022.110752
Wang, H., & Li, Y.-F. (2023, September). Robust Mechanical Fault Diagnosis With Noisy Label Based on Multistage True Label Distribution Learning. IEEE Transactions on Reliability, 72(3), 975–988. doi: 10.1109/TR.2022.3190942
Wang, H., Liu, Z., Peng, D., & Cheng, Z. (2022, September). Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Transactions, 128, 470–484. doi: 10.1016/j.isatra.2021.11.028
Wang, H., Liu, Z., Peng, D., & Zuo, M. J. (2023, July). Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis. Mechanical Systems and Signal Processing, 195, 110314. doi: 10.1016/j.ymssp.2023.110314
Wang, J., Zhang, Y., Zhang, F., Li, W., Lv, S., Jiang, M., & Jia, L. (2021, August). Accuracy-improved bearing fault diagnosis method based on avmd theory and awpso-elm model. Measurement, 181, 109666. doi: 10.1016/j.measurement.2021.109666
Wang, Z., Lu, C., Wang, Z., Liu, H., & Fan, H. (2013, August). Fault diagnosis and health assessment for bearings using the mahalanobis–taguchi system based on emd-svd. Transactions of the Institute of Measurement and Control, 35(6), 798–807. doi: 10.1177/0142331212472929
Yan, X., Yan, W.-J., Xu, Y., & Yuen, K.-V. (2023, November). Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. Mechanical Systems and Signal Processing, 202, 110664. doi: 10.1016/j.ymssp.2023.110664
Yu, X., Wang, Y., Liang, Z., Shao, H., Yu, K., & Yu, W. (2023). An Adaptive Domain Adaptation Method for Rolling Bearings’ Fault Diagnosis Fusing Deep Convolution and Self-Attention Networks. IEEE Transactions on Instrumentation and Measurement, 72, 1–14. doi: 10.1109/TIM.2023.3246494
Zhang, J., Zhang, K., An, Y., Luo, H., & Yin, S. (2024, May). An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition. IEEE Transactions on Neural Networks and Learning Systems, 35(5), 6231–6242. doi: 10.1109/TNNLS.2022.3232147
Zhang, W., Chen, D., Xiao, Y., & Yin, H. (2023, October). Semi-Supervised Contrast Learning Based on Multiscale Attention and Multitarget Contrast Learning for Bearing Fault Diagnosis. IEEE Transactions on Industrial Informatics, 19(10), 10056–10068. doi: 10.1109/TII.2023.3233960
Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017, February). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17(2), 425. doi: 10.3390/s17020425
Zhang, X., Sheng, C., Ouyang, W., & Zheng, L. (2023, June). Fault diagnosis of marine electric thruster bearing based on fusing multi-sensor deep learning models. Measurement, 214, 112727. doi: 10.1016/j.measurement.2023.112727
Zhao, Y.-P., & Chen, Y.-B. (2022, February). Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerospace Science and Technology, 121, 107311. doi: 10.1016/j.ast.2021.107311
Zheng, X., Nie, J., He, Z., Li, P., Dong, Z., & Gao, M. (2024, March). A fine-grained feature decoupling based multisource domain adaptation network for rotating machinery fault diagnosis. Reliability Engineering & System Safety, 243, 109892. doi: 10.1016/j.ress.2023.109892
Case western reserve university bearing fault data, available: https://engineering.case.edu/bearingdatacenter/downloaddata-file. (2019, December). doi: https://engineering.case.edu/bearingdatacenter/downloaddata-file
Chang, R., Ma, Y., Nie, W., Nie, J., Zhu, Y., & Liu, A.-A. (2024). Causal Disentanglement-Based Hidden Markov Model for Cross-Domain Bearing Fault Diagnosis. IEEE Transactions on Neural Networks and Learning Systems, 1–15. doi: 10.1109/TNNLS.2024.3513329
Chen, X., Zhang, B., & Gao, D. (2021, April). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987. doi: 10.1007/s10845-020-01600-2
Dong, Y., Jiang, H., Yao, R., Mu, M., & Yang, Q. (2024, March). Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning. Reliability Engineering & System Safety, 243, 109805. doi: 10.1016/j.ress.2023.109805
Fernández-Francos, D., Martínez-Rego, D., Fontenla-Romero, O., & Alonso-Betanzos, A. (2013, January). Automatic bearing fault diagnosis based on one-class ν-svm. Computers & Industrial Engineering, 64(1), 357–365. doi: 10.1016/j.cie.2012.10.013
He, K., Zhang, X., Ren, S., & Sun, J. (2016, June). Deep residual learning for image recognition. In 2016 ieee conference on computer vision and pattern recognition (cvpr) (pp. 770–778). Las Vegas, NV, USA: IEEE. doi: 10.1109/CVPR.2016.90
Hou, Z.-G., Wang, H.-W., Lv, S.-L., Xiong, M.-L., & Peng, K. (2022, December). Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition. Measurement Science and Technology, 34(3), 035109. doi: 10.1088/1361-6501/aca044
Huo, C., Jiang, Q., Shen, Y., Zhu, Q., & Zhang, Q. (2023, May). Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network. Engineering Applications of Artificial Intelligence, 121, 105970. doi: 10.1016/j.engappai.2023.105970
Kabla, A., & Mokrani, K. (2016). Bearing fault diagnosis using hilbert-huang transform (hht) and support vector machine (svm). Mechanics & Industry, 17(3), 308. doi: 10.1051/meca/2015067
Kingma, D. P., & Ba, J. (2017, January). Adam: A Method for Stochastic Optimization (No. arXiv:1412.6980). arXiv. doi: 10.48550/arXiv.1412.6980
Lai, Z., Yang, C., Lan, S., Wang, L., Shen, W., & Zhu, L. (2024, June). BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning. International Journal of Production Economics, 109319. doi: 10.1016/j.ijpe.2024.109319
Lei, Z., Zhang, P., Chen, Y., Feng, K., Wen, G., Liu, Z., . . . Yang, C. (2023, October). Prior knowledge-embedded meta transfer learning for few-shot fault diagnosis under variable operating conditions. Mechanical Systems and Signal Processing, 200, 110491. doi: 10.1016/j.ymssp.2023.110491
Li, X., Wang, Y., Yao, J., Li, M., & Gao, Z. (2024, May). Multi-sensor fusion fault diagnosis method of wind turbine bearing based on adaptive convergent viewable neural networks. Reliability Engineering & System Safety, 245, 109980. doi: 10.1016/j.ress.2024.109980
Lu, T., Yu, F., Han, B., & Wang, J. (2020). A generic intelligent bearing fault diagnosis system using convolutional neural networks with transfer learning. IEEE Access, 8, 164807–164814. doi: 10.1109/ACCESS.2020.3022840
Luo, M., Li, C., Zhang, X., Li, R., & An, X. (2016, November). Compound feature selection and parameter optimization of elm for fault diagnosis of rolling element bearings. ISA Transactions, 65, 556–566. doi: 10.1016/j.isatra.2016.08.022
Miao, M., Yu, J., & Zhao, Z. (2022, March). A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions. Reliability Engineering & System Safety, 219, 108259. doi: 10.1016/j.ress.2021.108259
Ni, Q., Ji, J. C., Feng, K., Zhang, Y., Lin, D., & Zheng, J. (2024, February). Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit. Reliability Engineering & System Safety, 242, 109753. doi: 10.1016/j.ress.2023.109753
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., . . . Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems (Vol. 32). Curran Associates, Inc.
Peng, D., Wang, H., Desmet, W., & Gryllias, K. (2023, April). RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability. Journal of Dynamics, Monitoring and Diagnostics. doi: 10.37965/jdmd.2023.156
Peng, H., Du, J., Gao, J.,Wang, Y., &Wang,W. (2024, May). Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis. Measurement Science and Technology, 35(5), 056204. doi: 10.1088/1361-6501/ad2828
Peng, H., Wang, W., Gao, J., Wang, Y., & Du, J. (2025). A Lightweight Triple-Stream Network With Multisensor Fusion for Enhanced Few-Shot Learning Fault Diagnosis. IEEE Transactions on Reliability, 1–14. doi: 10.1109/TR.2025.3540500
Qian, Q., Qin, Y., Luo, J., Wang, Y., & Wu, F. (2023, March). Deep discriminative transfer learning network for cross-machine fault diagnosis. Mechanical Systems and Signal Processing, 186, 109884. doi: 10.1016/j.ymssp.2022.109884
Rai, V. K., & Mohanty, A. R. (2007, August). Bearing fault diagnosis using fft of intrinsic mode functions in hilbert–huang transform. Mechanical Systems and Signal Processing, 21(6), 2607–2615. doi: 10.1016/j.ymssp.2006.12.004
Shao, X., & Kim, C.-S. (2024, February). Adaptive multiscale attention convolution neural network for crossdomain fault diagnosis. Expert Systems with Applications, 236, 121216. doi: 10.1016/j.eswa.2023.121216
Tang, H., Tang, Y., Su, Y., Feng, W., Wang, B., Chen, P., & Zuo, D. (2024, January). Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter. Engineering Applications of Artificial Intelligence, 127, 107138. doi: 10.1016/j.engappai.2023.107138
Wan, L., Li, Y., Chen, K., Gong, K., & Li, C. (2022, March). A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis. Measurement, 191, 110752. doi: 10.1016/j.measurement.2022.110752
Wang, H., & Li, Y.-F. (2023, September). Robust Mechanical Fault Diagnosis With Noisy Label Based on Multistage True Label Distribution Learning. IEEE Transactions on Reliability, 72(3), 975–988. doi: 10.1109/TR.2022.3190942
Wang, H., Liu, Z., Peng, D., & Cheng, Z. (2022, September). Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Transactions, 128, 470–484. doi: 10.1016/j.isatra.2021.11.028
Wang, H., Liu, Z., Peng, D., & Zuo, M. J. (2023, July). Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis. Mechanical Systems and Signal Processing, 195, 110314. doi: 10.1016/j.ymssp.2023.110314
Wang, J., Zhang, Y., Zhang, F., Li, W., Lv, S., Jiang, M., & Jia, L. (2021, August). Accuracy-improved bearing fault diagnosis method based on avmd theory and awpso-elm model. Measurement, 181, 109666. doi: 10.1016/j.measurement.2021.109666
Wang, Z., Lu, C., Wang, Z., Liu, H., & Fan, H. (2013, August). Fault diagnosis and health assessment for bearings using the mahalanobis–taguchi system based on emd-svd. Transactions of the Institute of Measurement and Control, 35(6), 798–807. doi: 10.1177/0142331212472929
Yan, X., Yan, W.-J., Xu, Y., & Yuen, K.-V. (2023, November). Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network. Mechanical Systems and Signal Processing, 202, 110664. doi: 10.1016/j.ymssp.2023.110664
Yu, X., Wang, Y., Liang, Z., Shao, H., Yu, K., & Yu, W. (2023). An Adaptive Domain Adaptation Method for Rolling Bearings’ Fault Diagnosis Fusing Deep Convolution and Self-Attention Networks. IEEE Transactions on Instrumentation and Measurement, 72, 1–14. doi: 10.1109/TIM.2023.3246494
Zhang, J., Zhang, K., An, Y., Luo, H., & Yin, S. (2024, May). An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition. IEEE Transactions on Neural Networks and Learning Systems, 35(5), 6231–6242. doi: 10.1109/TNNLS.2022.3232147
Zhang, W., Chen, D., Xiao, Y., & Yin, H. (2023, October). Semi-Supervised Contrast Learning Based on Multiscale Attention and Multitarget Contrast Learning for Bearing Fault Diagnosis. IEEE Transactions on Industrial Informatics, 19(10), 10056–10068. doi: 10.1109/TII.2023.3233960
Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017, February). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17(2), 425. doi: 10.3390/s17020425
Zhang, X., Sheng, C., Ouyang, W., & Zheng, L. (2023, June). Fault diagnosis of marine electric thruster bearing based on fusing multi-sensor deep learning models. Measurement, 214, 112727. doi: 10.1016/j.measurement.2023.112727
Zhao, Y.-P., & Chen, Y.-B. (2022, February). Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerospace Science and Technology, 121, 107311. doi: 10.1016/j.ast.2021.107311
Zheng, X., Nie, J., He, Z., Li, P., Dong, Z., & Gao, M. (2024, March). A fine-grained feature decoupling based multisource domain adaptation network for rotating machinery fault diagnosis. Reliability Engineering & System Safety, 243, 109892. doi: 10.1016/j.ress.2023.109892
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Technical Papers