Bearing Remaining Useful Life Prediction based on TSDAE and Pathformer-TCLSTM
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Published
Oct 6, 2025
Guanghua Fu
Yujie Yang
Yonghui Liu
Xuegen Wang
Abstract
The remaining useful life (RUL) prediction of bearings is crucial for the stable operation and effective maintenance. Conventional RUL prediction approaches extract the restricted features that would affect the prediction results, and the computational efficacy is often influenced by the redundancy of the features and domain knowledge. To address these problems, this paper proposes a RUL prediction approach mainly based on temporal sparse denoising autoencoders (TSDAE) for feature selecting, and Pathformer-temporal convolutional long short-term memory (Pathformer-TCLSTM) for predicting. Firstly, the original signal is denoised via wavelet thresholding. Subsequently, the denoised signal is decomposed using empirical mode decomposition (EMD) to extract the features of the time-domain, frequency-domain, and time-frequency domain to resolve the problem of restricted features. Moreover, the TSDAE feature selection technique is implemented to eliminate redundant features and address the limitation of domain knowledge utilized in traditional feature selection. Finally, the Pathformer-TCLSTM model is adopted for RUL prediction, which captures the multi-scale global information, local information, and long-range dependency. The validation on the PHM2012 and XJTU-SY bearing datasets shows that the proposed model has satisfactory predictive performance.
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Keywords
Bearings, remaining useful life, Pathformer, feature selection, emperical mode decomposition
References
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Aminou, H., Youssoufa, M., Abba, A. A. A., & Gbadoubissa, Z. E. J. (2023). Review of wavelet denoising algorithms. Multimedia Tools and Applications, 82(27), 41539-41569. https://doi.org/10.1007/s11042-023-15127-0
Atashgahi, Z., Sokar, G., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R., & Pechenizkiy, M. (2022). Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Machine Learning, 111(1), 377-414. https://doi.org/10.1007/s10994-021-06063-x
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
Barraza-Barraza, D., Tercero-Gómez, V. G., Cordero-Franco, A. E., & Beruvides, M. T. (2020). Remaining useful life estimation based on detection of explosive changes: Analysis of bearing vibration. International Journal of Prognostics and Health Management, 11(1). https://doi.org/10.36001/IJPHM.2020.V11I1.2609
Cao, Y., Ding, Y., Jia, M., & Tian, R. (2021). A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliability Engineering and System Safety, 215, 107813. https://doi.org/10.1016/J.RESS.2021.107813
Carlos, F., & Gil, G. (2022). Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems, 63, 550-562. https://doi.org/10.1016/J.JMSY.2022.05.010
Chen, P., Zhang, Y., Cheng, Y., Shu, Y., Wang, Y., Wen, Q., Yang, B., & Guo, C. (2024). Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting. arXiv preprint arXiv:2402.05956.
Ding, Y., & Jia, M. (2022). Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. https://doi.org/10.1109/tim.2022.3181933
Dong, S., Xiao, J., Hu, X., Fang, N., Liu, L., & Yao, J. (2023). Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing. Reliability Engineering & System Safety, 230, 108914. https://doi.org/10.1016/J.RESS.2022.108914
Gao, Z., Jiang, W., Wu, J., & Dai, T. (2025). Multiscale Spatiotemporal Attention Network for Remaining Useful Life Prediction of Mechanical Systems. IEEE sensors journal, 1-1. https://doi.org/10.1109/JSEN.2024.3523176
Guo, J., Wang, J., Wang, Z., Gong, Y., Qi, J., Wang, G., & Tang, C. (2023). A CNN‐ BiLSTM‐ Bootstrap integrated method for remaining useful life prediction of rolling bearings. Quality and Reliability Engineering International, 39(5), 1796-1813. https://doi.org/10.1002/qre.3314
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98-109. https://doi.org/10.1016/j.neucom.2017.02.045
Guo, R., Wang, Y., Zhang, H., & Zhang, G. (2021). Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM. IEEE Transactions on Instrumentation and Measurement, 70, 1-12. https://doi.org/10.1109/tim.2021.3051717
Ioannides, E., & Harris, T. A. (1985). A new fatigue life model for rolling bearings. 107, 367-377. https://doi.org/10.1115/1.3261081
Li, J. (2017). Feature selection: A data perspective. Comput. Surveys, 50, 6. https://doi.org/10.1145/3136625
Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, 182, 208-218. https://doi.org/10.1016/j.ress.2018.11.011
Li, Y., Huang, X., Zhao, C., & Ding, P. (2022). A novel remaining useful life prediction method based on multi-support vector regression fusion and adaptive weight updating. ISA transactions, 131, 444-459. https://doi.org/10.1016/j.isatra.2022.04.042
Ma, M., & Mao, Z. (2020). Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Transactions on Industrial Informatics, 17(3), 1658-1667. https://doi.org/10.1109/tii.2020.2991796
Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012). Remaining useful life estimation of critical components with application to bearings. IEEE Transactions on reliability, 61(2), 292-302. https://doi.org/10.1109/tr.2012.2194175
Motahari-Nezhad, M., & Jafari, S. M. (2021). Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing. Expert Systems With Applications, 168, 114391. https://doi.org/10.1016/j.eswa.2020.114391
Najdi, B., Benbrahim, M., & Kabbaj, M. N. (2025). Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings. International Journal of Prognostics and Health Management, 16(1). https://doi.org/10.36001/ijphm.2025.v16i1.4171
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, PHM'12.,
Paris, P., & Erdogan, F. (1963). A critical analysis of crack propagation laws. 85(4), 528-533. https://doi.org/10.1115/1.3656900
Peng, H., Jiang, B., Mao, Z., & Liu, S. (2023). Local enhancing transformer with temporal convolutional attention mechanism for bearings remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 72, 1-12. https://doi.org/10.1109/tim.2023.3291787
Qiu, H., Niu, Y., Shang, J., Gao, L., & Xu, D. (2023). A piecewise method for bearing remaining useful life estimation using temporal convolutional networks. Journal of Manufacturing Systems, 68, 227-241. https://doi.org/10.1016/j.jmsy.2023.04.002
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71-77. https://doi.org/10.1016/j.jmsy.2018.04.008
Saufi, M. S. R. M., & Hassan, K. A. (2021). Remaining useful life prediction using an integrated Laplacian- LSTM network on machinery components. Applied Soft Computing, 112, 107817. https://doi.org/10.1016/J.ASOC.2021.107817
Sun, C., Ma, M., Zhao, Z., Tian, S., Yan, R., & Chen, X. (2019). Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing. IEEE Transactions on Industrial Informatics, 15(4), 2416-2425. https://doi.org/10.1109/TII.2018.2881543
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1706.03762
wahhab Lourari, A., Benkedjouh, T., El Yousfi, B., & Soualhi, A. (2024). An anfis-based framework for the prediction of bearing’s remaining useful life. International Journal of Prognostics and Health Management, 15(1). https://doi.org/10.36001/ijphm.2024.v15i1.3791
Wang, B., Lei, Y., Li, N., & Li, N. (2018). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on reliability, 69(1), 401-412. https://doi.org/10.1109/tr.2018.2882682
Wang, H., Wang, D., Liu, H., & Tang, G. (2022). A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation. Reliability Engineering and System Safety, 225, 108601. https://doi.org/10.1016/J.RESS.2022.108601
Wang, H., Yang, J., Wang, R., & Shi, L. (2023). Remaining useful life prediction of bearings based on convolution attention mechanism and temporal convolution network. IEEE Access, 11, 24407-24419. https://doi.org/10.1109/access.2023.3255891
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. Iop Conference, 324, 012049. https://doi.org/10.1088/1757-899X/324/1/012049
Wang, Y., Zhao, J., Yang, C., Xu, D., & Ge, J. (2022). Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion. Measurement, 201, 111572. https://doi.org/10.1016/J.MEASUREMENT.2022.111572
Wang, Y., Zhao, Y., & Addepalli, S. (2020). Remaining useful life prediction using deep learning approaches: A review. Procedia manufacturing, 49, 81-88. https://doi.org/10.1016/j.promfg.2020.06.015
Wen, L., Su, S., Li, X., Ding, W., & Feng, K. (2024). GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation. Mechanical systems and signal processing, 220, 111663. https://doi.org/10.1016/j.ymssp.2024.111663
Yang, W., Yao, Q., Ye, K., & Xu, C.-Z. (2020). Empirical mode decomposition and temporal convolutional networks for remaining useful life estimation. International journal of parallel programming, 48(1), 61-79. https://doi.org/10.1007/s10766-019-00650-1
Yang, X., Chen, D., Huang, J., Wu, X., Chen, Z., & Li, Q. (2025). Remaining Useful Life Prediction Under Multiple Operating Conditions Based on a Novel Dual-Layer Temporal Convolutional Network. IEEE sensors journal, 25(1), 1900-1911. https://doi.org/10.1109/JSEN.2024.3494020
Yang, Y., Wu, Q. M. J., & Wang, Y. (2016). Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(7), 1065-1079. https://doi.org/10.1109/TSMC.2016.2637279
Yao, D., Li, B., Liu, H., Yang, J., & Jia, L. (2021). Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit. Measurement, 175, 109166. https://doi.org/10.1016/j.measurement.2021.109166
Ye, Y., Wang, J., Yang, J., Yao, D., & Zhou, T. (2024). Adaptive MAGNN-TCN: An Innovative Approach for Bearings Remaining Useful Life Prediction. IEEE sensors journal, 1-1. https://doi.org/10.1109/JSEN.2024.3506154
Yu, W., Pi, D., Xie, L., & Luo, Y. (2021). Multiscale attentional residual neural network framework for remaining useful life prediction of bearings. Measurement, 177, 109310. https://doi.org/10.1016/J.MEASUREMENT.2021.109310
Zhang, H., Zhang, Q., Shao, S., Niu, T., & Yang, X. (2020). Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction. IEEE Access, 8, 132188-132199. https://doi.org/10.1109/access.2020.3010066
Zhao, H., Liu, H., Jin, Y., Dang, X., & Deng, W. (2021). Feature extraction for data-driven remaining useful life prediction of rolling bearings. IEEE Transactions on Instrumentation and Measurement, 70, 1-10. https://doi.org/10.1109/tim.2021.3059500
Zhu, G., Zhu, Z., Xiang, L., Hu, A., & Xu, Y. (2023). Prediction of bearing remaining useful life based on DACN-ConvLSTM model. Measurement, 211, 112600. https://doi.org/10.1016/J.MEASUREMENT.2023.112600
Zhu, J., Chen, N., & Shen, C. (2020). A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical systems and signal processing, 139, 106602. https://doi.org/10.1016/j.ymssp.2019.106602
Aminou, H., Youssoufa, M., Abba, A. A. A., & Gbadoubissa, Z. E. J. (2023). Review of wavelet denoising algorithms. Multimedia Tools and Applications, 82(27), 41539-41569. https://doi.org/10.1007/s11042-023-15127-0
Atashgahi, Z., Sokar, G., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R., & Pechenizkiy, M. (2022). Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders. Machine Learning, 111(1), 377-414. https://doi.org/10.1007/s10994-021-06063-x
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
Barraza-Barraza, D., Tercero-Gómez, V. G., Cordero-Franco, A. E., & Beruvides, M. T. (2020). Remaining useful life estimation based on detection of explosive changes: Analysis of bearing vibration. International Journal of Prognostics and Health Management, 11(1). https://doi.org/10.36001/IJPHM.2020.V11I1.2609
Cao, Y., Ding, Y., Jia, M., & Tian, R. (2021). A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliability Engineering and System Safety, 215, 107813. https://doi.org/10.1016/J.RESS.2021.107813
Carlos, F., & Gil, G. (2022). Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods. Journal of Manufacturing Systems, 63, 550-562. https://doi.org/10.1016/J.JMSY.2022.05.010
Chen, P., Zhang, Y., Cheng, Y., Shu, Y., Wang, Y., Wen, Q., Yang, B., & Guo, C. (2024). Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting. arXiv preprint arXiv:2402.05956.
Ding, Y., & Jia, M. (2022). Convolutional transformer: An enhanced attention mechanism architecture for remaining useful life estimation of bearings. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. https://doi.org/10.1109/tim.2022.3181933
Dong, S., Xiao, J., Hu, X., Fang, N., Liu, L., & Yao, J. (2023). Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing. Reliability Engineering & System Safety, 230, 108914. https://doi.org/10.1016/J.RESS.2022.108914
Gao, Z., Jiang, W., Wu, J., & Dai, T. (2025). Multiscale Spatiotemporal Attention Network for Remaining Useful Life Prediction of Mechanical Systems. IEEE sensors journal, 1-1. https://doi.org/10.1109/JSEN.2024.3523176
Guo, J., Wang, J., Wang, Z., Gong, Y., Qi, J., Wang, G., & Tang, C. (2023). A CNN‐ BiLSTM‐ Bootstrap integrated method for remaining useful life prediction of rolling bearings. Quality and Reliability Engineering International, 39(5), 1796-1813. https://doi.org/10.1002/qre.3314
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98-109. https://doi.org/10.1016/j.neucom.2017.02.045
Guo, R., Wang, Y., Zhang, H., & Zhang, G. (2021). Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM. IEEE Transactions on Instrumentation and Measurement, 70, 1-12. https://doi.org/10.1109/tim.2021.3051717
Ioannides, E., & Harris, T. A. (1985). A new fatigue life model for rolling bearings. 107, 367-377. https://doi.org/10.1115/1.3261081
Li, J. (2017). Feature selection: A data perspective. Comput. Surveys, 50, 6. https://doi.org/10.1145/3136625
Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering & System Safety, 182, 208-218. https://doi.org/10.1016/j.ress.2018.11.011
Li, Y., Huang, X., Zhao, C., & Ding, P. (2022). A novel remaining useful life prediction method based on multi-support vector regression fusion and adaptive weight updating. ISA transactions, 131, 444-459. https://doi.org/10.1016/j.isatra.2022.04.042
Ma, M., & Mao, Z. (2020). Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Transactions on Industrial Informatics, 17(3), 1658-1667. https://doi.org/10.1109/tii.2020.2991796
Medjaher, K., Tobon-Mejia, D. A., & Zerhouni, N. (2012). Remaining useful life estimation of critical components with application to bearings. IEEE Transactions on reliability, 61(2), 292-302. https://doi.org/10.1109/tr.2012.2194175
Motahari-Nezhad, M., & Jafari, S. M. (2021). Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing. Expert Systems With Applications, 168, 114391. https://doi.org/10.1016/j.eswa.2020.114391
Najdi, B., Benbrahim, M., & Kabbaj, M. N. (2025). Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings. International Journal of Prognostics and Health Management, 16(1). https://doi.org/10.36001/ijphm.2025.v16i1.4171
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, PHM'12.,
Paris, P., & Erdogan, F. (1963). A critical analysis of crack propagation laws. 85(4), 528-533. https://doi.org/10.1115/1.3656900
Peng, H., Jiang, B., Mao, Z., & Liu, S. (2023). Local enhancing transformer with temporal convolutional attention mechanism for bearings remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 72, 1-12. https://doi.org/10.1109/tim.2023.3291787
Qiu, H., Niu, Y., Shang, J., Gao, L., & Xu, D. (2023). A piecewise method for bearing remaining useful life estimation using temporal convolutional networks. Journal of Manufacturing Systems, 68, 227-241. https://doi.org/10.1016/j.jmsy.2023.04.002
Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71-77. https://doi.org/10.1016/j.jmsy.2018.04.008
Saufi, M. S. R. M., & Hassan, K. A. (2021). Remaining useful life prediction using an integrated Laplacian- LSTM network on machinery components. Applied Soft Computing, 112, 107817. https://doi.org/10.1016/J.ASOC.2021.107817
Sun, C., Ma, M., Zhao, Z., Tian, S., Yan, R., & Chen, X. (2019). Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing. IEEE Transactions on Industrial Informatics, 15(4), 2416-2425. https://doi.org/10.1109/TII.2018.2881543
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. https://doi.org/10.48550/arXiv.1706.03762
wahhab Lourari, A., Benkedjouh, T., El Yousfi, B., & Soualhi, A. (2024). An anfis-based framework for the prediction of bearing’s remaining useful life. International Journal of Prognostics and Health Management, 15(1). https://doi.org/10.36001/ijphm.2024.v15i1.3791
Wang, B., Lei, Y., Li, N., & Li, N. (2018). A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Transactions on reliability, 69(1), 401-412. https://doi.org/10.1109/tr.2018.2882682
Wang, H., Wang, D., Liu, H., & Tang, G. (2022). A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation. Reliability Engineering and System Safety, 225, 108601. https://doi.org/10.1016/J.RESS.2022.108601
Wang, H., Yang, J., Wang, R., & Shi, L. (2023). Remaining useful life prediction of bearings based on convolution attention mechanism and temporal convolution network. IEEE Access, 11, 24407-24419. https://doi.org/10.1109/access.2023.3255891
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. Iop Conference, 324, 012049. https://doi.org/10.1088/1757-899X/324/1/012049
Wang, Y., Zhao, J., Yang, C., Xu, D., & Ge, J. (2022). Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion. Measurement, 201, 111572. https://doi.org/10.1016/J.MEASUREMENT.2022.111572
Wang, Y., Zhao, Y., & Addepalli, S. (2020). Remaining useful life prediction using deep learning approaches: A review. Procedia manufacturing, 49, 81-88. https://doi.org/10.1016/j.promfg.2020.06.015
Wen, L., Su, S., Li, X., Ding, W., & Feng, K. (2024). GRU-AE-wiener: A generative adversarial network assisted hybrid gated recurrent unit with Wiener model for bearing remaining useful life estimation. Mechanical systems and signal processing, 220, 111663. https://doi.org/10.1016/j.ymssp.2024.111663
Yang, W., Yao, Q., Ye, K., & Xu, C.-Z. (2020). Empirical mode decomposition and temporal convolutional networks for remaining useful life estimation. International journal of parallel programming, 48(1), 61-79. https://doi.org/10.1007/s10766-019-00650-1
Yang, X., Chen, D., Huang, J., Wu, X., Chen, Z., & Li, Q. (2025). Remaining Useful Life Prediction Under Multiple Operating Conditions Based on a Novel Dual-Layer Temporal Convolutional Network. IEEE sensors journal, 25(1), 1900-1911. https://doi.org/10.1109/JSEN.2024.3494020
Yang, Y., Wu, Q. M. J., & Wang, Y. (2016). Autoencoder With Invertible Functions for Dimension Reduction and Image Reconstruction. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(7), 1065-1079. https://doi.org/10.1109/TSMC.2016.2637279
Yao, D., Li, B., Liu, H., Yang, J., & Jia, L. (2021). Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit. Measurement, 175, 109166. https://doi.org/10.1016/j.measurement.2021.109166
Ye, Y., Wang, J., Yang, J., Yao, D., & Zhou, T. (2024). Adaptive MAGNN-TCN: An Innovative Approach for Bearings Remaining Useful Life Prediction. IEEE sensors journal, 1-1. https://doi.org/10.1109/JSEN.2024.3506154
Yu, W., Pi, D., Xie, L., & Luo, Y. (2021). Multiscale attentional residual neural network framework for remaining useful life prediction of bearings. Measurement, 177, 109310. https://doi.org/10.1016/J.MEASUREMENT.2021.109310
Zhang, H., Zhang, Q., Shao, S., Niu, T., & Yang, X. (2020). Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction. IEEE Access, 8, 132188-132199. https://doi.org/10.1109/access.2020.3010066
Zhao, H., Liu, H., Jin, Y., Dang, X., & Deng, W. (2021). Feature extraction for data-driven remaining useful life prediction of rolling bearings. IEEE Transactions on Instrumentation and Measurement, 70, 1-10. https://doi.org/10.1109/tim.2021.3059500
Zhu, G., Zhu, Z., Xiang, L., Hu, A., & Xu, Y. (2023). Prediction of bearing remaining useful life based on DACN-ConvLSTM model. Measurement, 211, 112600. https://doi.org/10.1016/J.MEASUREMENT.2023.112600
Zhu, J., Chen, N., & Shen, C. (2020). A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions. Mechanical systems and signal processing, 139, 106602. https://doi.org/10.1016/j.ymssp.2019.106602
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Technical Papers