Abstract
The complexity of the internal structure of rolling bearings and the harshness of their operating environment result in strong non-stationarity and nonlinearity of the vibration signals. It remains a challenging and attractive task to accomplish more accurate classification through signal processing techniques and pattern recognition methods. To realize this aim, a novel one-dimensional improved self-attention-enhanced convolutional neural network (1D-ISACNN) with empirical wavelet transform (EWT) is proposed for rolling bearing fault classification. Firstly, the EWT algorithm is employed to decompose the raw signal into three frequency components, allowing for further extraction of multi-frequency components to enhance signal characteristics. Subsequently, a creative1D-ISACNN leverages the merits of a newly developed attention mechanism and an optimized meta-activation concatenation function in feature learning to better capture and map crucial information within the signal. Furthermore, label smoothing regularization is designed as the loss function of the 1D-ISACNN, which takes into account not only the loss of correctly labeled positions in the training samples but also the loss of other mislabeled positions. Finally, the adaptive moment projection estimation is designed to ensure a more robust gradient update strategy for updating the parameters of the proposed model. The developed model tested on three different sets of bearing data, has achieved a classification accuracy of 100%. In ablative experiments and other comparative experiments, the proposed method demonstrates higher recognition accuracy and more robust generalization capabilities compared to other excellent approaches.
Similar content being viewed by others
Data availability
The datasets used during the current study are available from the corresponding author upon reasonable request. We have already cited relevant articles for the data used in the article.
References
Weng, C., Lu, B., Gu, Q., Zhao, X.: A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions. Nonlinear Dyn. 111(12), 11315–11334 (2023)
Zhao, D., Li, J., Cheng, W., Wen, W.: Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions. ISA Trans. 133, 518–528 (2023)
Wang, C., Liu, J., Zio, E.: A modified generative adversarial network for fault diagnosis in high-speed train components with imbalanced and heterogeneous monitoring data. J. Dyn. Monit. Diagn. 1, 84–92 (2022)
An, F., Wang, J.: Rolling bearing fault diagnosis algorithm using overlap** group sparse-deep complex convolutional neural network. Nonlinear Dyn. 108(3), 2353–2368 (2022)
Zhao, D., Huang, X., Cui, L.: Horizontal reassigning transform for bearing fault impulses characterizing. IEEE Sens. J. 24, 1837–1846 (2023)
Zhao, D., Wang, H., Cui, L.: Frequency-chirprate synchrosqueezing-based scaling chirplet transform for wind turbine nonstationary fault feature time–frequency representation. Mech. Syst. Signal Proc. 209, 111112 (2024)
Huang, J., Cui, L.: Tensor singular spectrum decomposition: multisensor denoising algorithm and application. IEEE Trans. Instrum. Meas. 72, 1–15 (2023)
Zhao, D., Cui, L., Liu, D.: Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform. IEEE/ASME Trans. Mechatron. 28(3), 1627–1637 (2023)
Liu, D., Cui, L., Cheng, W.: A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication. Meas. Sci. Technol. 35(1), 012002 (2024)
Dong, Z., Zhao, D., Cui, L.: Non-negative wavelet matrix factorization-based bearing fault intelligent classification method. Meas. Sci. Technol. 34(11), 115013 (2023)
Wang, G., Liu, D., Cui, L.: Auto-embedding transformer for interpretable few-shot fault diagnosis of rolling bearings. IEEE Trans. Reliab. (2023). https://doi.org/10.1109/TR.2023.3328597
He, D., Lao, Z., **, Z., He, C., Shan, S., Miao, J.: Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network. Nonlinear Dyn. 111, 1–24 (2023)
Zhao, B., Zhang, X., Li, H., Yang, Z.: Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions. Knowl. Based Syst. 199, 105971 (2020)
Ruan, D., Wang, J., Yan, J., Gühmann, C.: CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis. Adv. Eng. Inform. 55, 101877 (2023)
Lv, H., Chen, J., Pan, T., Zhang, T., Feng, Y., Liu, S.: Attention mechanism in intelligent fault diagnosis of machinery: a review of technique and application. Measurement 199, 111594 (2022)
Chang, M., Yao, D., Yang, J.: Intelligent fault diagnosis of rolling bearings using efficient and lightweight ResNet networks based on an attention mechanism (September 2022). IEEE Sens. J. 23, 9136–9145 (2023)
Wang, H., Liu, Z., Peng, D., Yang, M., Qin, Y.: Feature-level attention-guided multitask CNN for fault diagnosis and working conditions identification of rolling bearing. IEEE Trans. Neural Netw. Learn. Syst. 33(9), 4757–4769 (2021)
Zou, F., Zhang, H., Sang, S., Li, X., He, W., Liu, X., Chen, Y.: An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis. Measurement 186, 110236 (2021)
Tan, C., Yang, L., Chen, H., **n, L.: Fault diagnosis method for rolling bearing based on VMD and improved SVM optimized by METLBO. J. Mech. Sci. Technol. 36(10), 4979–4991 (2022)
**, Z., Chen, D., He, D., Sun, Y., Yin, X.: Bearing fault diagnosis based on VMD and improved CNN. J. Fail. Anal. Prev. 23(1), 165–175 (2023)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates Inc, Red Hook (2012)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). ar**v preprint https://arxiv.org/abs/1412.6980
Jiang, W., Li, Z., Zhang, S., Wang, T., Zhang, S.: Hydraulic pump fault diagnosis method based on EWT decomposition denoising and deep learning on cloud platform. Shock. Vib. 2021, 1–18 (2021)
Wu, H., Li, Z., Tang, Q., Zhang, P., **a, D., Zhao, L.: A practical prediction method for grinding accuracy based on multi-source data fusion in manufacturing. Int. J. Adv. Manuf. Technol. 127, 1–11 (2023)
Yao, Y., Zhang, S., Yang, S., Gui, G.: Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions. Sensors 20(4), 1233 (2020)
Ma, N., Zhang, X., Liu, M., Sun, J.: Activate or not: learning customized activation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8032–8042 (2021)
Liu, J., Wang, X., Wu, S., Wan, L., **e, F.: Wind turbine fault detection based on deep residual networks. Expert Syst. Appl. 213, 119102 (2023)
Qin, H., Pan, J., Li, J., Huang, F.: Fault diagnosis method of rolling bearing based on CBAM_ResNet and ACON activation function. Appl. Sci. 13(13), 7593 (2023)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Heo, B., Chun, S., Oh, S.J., Han, D., Yun, S., Kim, G., Uh, Y., Ha, J.W.: Adamp: slowing down the slowdown for momentum optimizers on scale-invariant weights (2020). ar**v preprint https://arxiv.org/abs/2006.08217
Wang, X., Cui, L., Wang, H., Jiang, H.: A generalized health indicator for performance degradation assessment of rolling element bearings based on graph spectrum reconstruction and spectrum characterization. Measurement 176, 109165 (2021)
Smith, W.A., Randall, R.B.: Rolling element bearing diagnostics using the case western reserve University data: a benchmark study. Mech. Syst. Signal Process. 64, 100–131 (2015)
Shao, H., **a, M., Han, G., Zhang, Y., Wan, J.: Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images. IEEE Trans. Ind. Inform. 17(5), 3488–3496 (2020)
Acknowledgements
The work is supported by the National Natural Science Foundation of China (Grant Nos. 52075008 and 51905292) and Bei**g Municipal Natural Science Foundation under Grant No. L221007.
Funding
The work is supported by the National Natural Science Foundation of China (Grant Nos. 52075008 and 51905292) and Bei**g Municipal Natural Science Foundation under Grant No. L221007.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dong, Z., Zhao, D. & Cui, L. An intelligent bearing fault diagnosis framework: one-dimensional improved self-attention-enhanced CNN and empirical wavelet transform. Nonlinear Dyn 112, 6439–6459 (2024). https://doi.org/10.1007/s11071-024-09389-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-024-09389-y