Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings
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Published
Dec 30, 2024
Boubker Najdi
Mohammed Benbrahim
Mohammed Nabil Kabbaj
Abstract
Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. These features are then fed into a Residual Network (ResNet) combined with a temporal attention layer, followed by a Long Short-Term Memory (LSTM) model, referred to as the Adaptive Residual Attention LSTM (ARAL), to assess the Health Indicator (HI) of the bearings. Notably, an exponential data labeling technique is employed instead of traditional linear labeling, enhancing the robustness of the HI assessment. Following the HI estimation, the three-sigma method is applied to identify the degradation starting point. Subsequently, Gaussian Process Regression (GPR) is utilized to predict the RUL from this point forward. The proposed method demonstrates superior performance compared to existing techniques, providing more accurate and reliable RUL predictions. Experimental results show that this integrated approach effectively captures the complex degradation patterns of bearings, making it a valuable tool for prognostics and health management in industrial applications.
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Keywords
Bearing RUL Prognosis, Signal Processing, Deep Learning, Adaptive Labeling, Predictive Maintenance
References
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Deng, Y., Guo, C., Zhang, Z., Zou, L., Liu, X., & Lin, S. (2023). An attention-based method for remaining useful life prediction of rotating machinery. Applied Sciences, 13(4).
Deutsch, J., & He, D. (2018). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 11-20. doi: 10.1109/TSMC.2017.2697842
Ding, Y., Jia, M., Cao, Y., Ding, P., Zhao, X., & Lee, C.-G. (2023). Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions. Knowledge-Based Systems, 261, 110199. doi: https://doi.org/10.1016/j.knosys.2022.110199
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Shah, A., Kadam, E., Shah, H., & Shinde, S. (2016, 04). Deep residual networks with exponential linear unit.
She, D., & Jia, M. (2021). A bigru method for remaining useful life prediction of machinery. Measurement, 167, 108277. doi: https://doi.org/10.1016/j.measurement.2020.108277
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Wang, B., Lei, Y., Li, N., & Li, N. (2020b). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401-412. doi: 10.1109/TR.2018.2882682
Wang, B., Lei, Y., Li, N., & Yan, T. (2019). Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 134, 106330.
Yan, H., Qin, Y., Xiang, S., Wang, Y., & Chen, H. (2020). Long-term gear life prediction based on ordered neurons lstm neural networks. Measurement, 165, 108205. doi: https://doi.org/10.1016/j.measurement.2020.108205
Yoo, Y., & Baek, J.-G. (2018). A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network. Applied Sciences, 8(7). doi: 10.3390/app8071102
Zhang, Y., Tang, B., Han, Y., & Deng, L. (2017, feb). Bearing performance degradation assessment based on time-frequency code features and som network. Measurement Science and Technology, 28(4), 045601. doi: 10.1088/1361-6501/aa56c9
Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bidirectional lstm networks. Sensors, 17(2).
Zhu, J., Chen, N., & Peng, W. (2019a). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208-3216. doi: 10.1109/TIE.2018.2844856
Zhu, J., Chen, N., & Peng, W. (2019b). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208-3216. doi: 10.1109/TIE.2018.2844856
Zhuang, J., Jia, M., Cao, Y., & Zhao, X. (2022). Semisupervised double attention guided assessment approach for remaining useful life of rotating machinery. Reliability Engineering & System Safety, 226, 108685. doi: https://doi.org/10.1016/j.ress.2022.108685
Chen, Z., Jin, X., Kong, Z., Wang, F., & Xu, Z. (2023). Global and local information integrated network for remaining useful life prediction. Engineering Applications of Artificial Intelligence, 126, 106956. doi: https://doi.org/10.1016/j.engappai.2023.106956
Cheng, Y., Hu, K.,Wu, J., Zhu, H., & Shao, X. (2021). A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings. Advanced Engineering Informatics, 48, 101247.
Daubechies, I., Lu, J., & Wu, H.-T. (2011). Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30(2), 243-261.
Deng, Y., Guo, C., Zhang, Z., Zou, L., Liu, X., & Lin, S. (2023). An attention-based method for remaining useful life prediction of rotating machinery. Applied Sciences, 13(4).
Deutsch, J., & He, D. (2018). Using deep learning-based approach to predict remaining useful life of rotating components. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(1), 11-20. doi: 10.1109/TSMC.2017.2697842
Ding, Y., Jia, M., Cao, Y., Ding, P., Zhao, X., & Lee, C.-G. (2023). Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions. Knowledge-Based Systems, 261, 110199. doi: https://doi.org/10.1016/j.knosys.2022.110199
Hong, S., Zhou, Z., Zio, E., & Hong, K. (2014). Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing, 27, 159-166. doi: https://doi.org/10.1016/j.dsp.2013.12.010
Johnson, W., & Lindenstrauss, J. (1984, 01). Extensions of lipschitz maps into a hilbert space. Contemporary Mathematics, 26, 189-206. doi: 10.1090/conm/026/737400
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical Systems and Signal Processing, 104, 799-834. doi: https://doi.org/10.1016/j.ymssp.2017.11.016
Li, L., Xu, J., & Li, J. (2023). Estimating remaining useful life of rotating machinery using relevance vector machine and deep learning network. Engineering Failure Analysis, 146, 107125. doi: https://doi.org/10.1016/j.engfailanal.2023.107125
Li, X., Yu, S., Lei, Y., Li, N., & Yang, B. (2024). Intelligent machinery fault diagnosis with event-based camera. IEEE Transactions on Industrial Informatics, 20(1), 380-389. doi: 10.1109/TII.2023.3262854
Liu, X., Song, P., Yang, C., Hao, C., & Peng, W. (2018, 07). Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation. IEEE Transactions on Industrial Electronics, PP, 1-1. doi: 10.1109/TIE.2017.2733469
Londhe, N. D., Arakere, N. K.,&Subhash, G. (2017, 09). Extended Hertz Theory of Contact Mechanics for Case-Hardened SteelsWith Implications for Bearing Fatigue Life. Journal of Tribology, 140(2), 021401. doi: 10.1115/1.4037359
Najdi, B., Benbrahim, M., & Kabbaj, M. N. (2024, March). Bearing fault diagnosis under varying work conditions based on synchrosqueezing transform, random projection, and convolutional neural networks. Int. J. Progn. Health Manag., 15(1). doi: 10.36001/IJPHM.2024.v15i1.3799
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012, 06). Pronostia: An experimental platform for bearings accelerated degradation tests. In (p. 1-8).
Pichler, K., Ooijevaar, T., Hesch, C., Kastl, C., & Hammer, F. (2020, 05). Data-driven vibration-based bearing fault diagnosis using non-steady-state training data. Journal of Sensors and Sensor Systems, 9, 143-155.
Qin, Y., Xiang, S., Chai, Y., & Chen, H. (2020). Macroscopic–microscopic attention in lstm networks based on fusion features for gear remaining life prediction. IEEE Transactions on Industrial Electronics, 67(12), 10865-10875. doi: 10.1109/TIE.2019.2959492
Que, Z., & Xu, Z. (2019). A data-driven health prognostics approach for steam turbines based on xgboost and dtw. IEEE Access, 7, 93131-93138. doi: 10.1109/ACCESS.2019.2927488
Sadowsky, J. S. (1994). The continuous wavelet transform: a tool for signal investigation and understanding.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (p. 1-9). doi: 10.1109/PHM.2008.4711414
Shah, A., Kadam, E., Shah, H., & Shinde, S. (2016, 04). Deep residual networks with exponential linear unit.
She, D., & Jia, M. (2021). A bigru method for remaining useful life prediction of machinery. Measurement, 167, 108277. doi: https://doi.org/10.1016/j.measurement.2020.108277
Singleton, R. K., Strangas, E. G., & Aviyente, S. (2015). Extended kalman filtering for remaining-useful-life estimation of bearings. IEEE Transactions on Industrial Electronics, 62(3), 1781 – 1790.
Suh, S., Lukowicz, P., & Lee, Y. O. (2022). Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks. Knowledge-Based Systems, 237, 107866. doi: https://doi.org/10.1016/j.knosys.2021.107866
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2023). Attention is all you need. Retrieved from https://arxiv.org/abs/1706.03762
Wang, B., Lei, Y., Li, N., & Li, N. (2020a). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401-412. doi: 10.1109/TR.2018.2882682
Wang, B., Lei, Y., Li, N., & Li, N. (2020b). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on Reliability, 69(1), 401-412. doi: 10.1109/TR.2018.2882682
Wang, B., Lei, Y., Li, N., & Yan, T. (2019). Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 134, 106330.
Yan, H., Qin, Y., Xiang, S., Wang, Y., & Chen, H. (2020). Long-term gear life prediction based on ordered neurons lstm neural networks. Measurement, 165, 108205. doi: https://doi.org/10.1016/j.measurement.2020.108205
Yoo, Y., & Baek, J.-G. (2018). A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network. Applied Sciences, 8(7). doi: 10.3390/app8071102
Zhang, Y., Tang, B., Han, Y., & Deng, L. (2017, feb). Bearing performance degradation assessment based on time-frequency code features and som network. Measurement Science and Technology, 28(4), 045601. doi: 10.1088/1361-6501/aa56c9
Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bidirectional lstm networks. Sensors, 17(2).
Zhu, J., Chen, N., & Peng, W. (2019a). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208-3216. doi: 10.1109/TIE.2018.2844856
Zhu, J., Chen, N., & Peng, W. (2019b). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208-3216. doi: 10.1109/TIE.2018.2844856
Zhuang, J., Jia, M., Cao, Y., & Zhao, X. (2022). Semisupervised double attention guided assessment approach for remaining useful life of rotating machinery. Reliability Engineering & System Safety, 226, 108685. doi: https://doi.org/10.1016/j.ress.2022.108685
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Technical Papers