Prediction of remaining useful life in bearings applying Machine Learning: A Systematic Literature Review
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Abstract
Los equipos de rotación son equipos que con mucha frecuencia se encuentran instalados en toda planta industrial y los rodamientos son los componentes que con mayor frecuencia fallan. Este motiva a que anticiparse a la ocurrencia del fallo en estos elementos, conlleve benéficamente a la reducción de pérdidas ocasionado por estas averías. En tal virtud, realizar un estudio de revisión sistemática de literatura (LSR), que permita conocer cuáles son los principales problemas que abordan las investigaciones en el campo de predicción de la vida útil remanente en rodamientos, así como identificar cuáles son los modelos de Machine Learning más empleados, resulta relevante. Para el desarrollo de este estudio se aplicó la metodología PRISMA, y el protocolo de Kitchenham para garantizar la confiabilidad de los resultados. Como resultado de la etapa selección de información se identificaron 35 artículos publicados en el periodo de 2018 a 2021, los cuales fueron sometidos a análisis. Se identificaron tres problemas que abordan los diferentes estudios: la extracción de características, la identificación de la etapa de degradación y la implementación de modelos generalizables. Los modelos más empleados corresponden al campo de Deep Learning.
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Villacrés Parra, S. R., Zavala León, M. A., & Viscaíno Cuzco, M. A. (2024). Prediction of remaining useful life in bearings applying Machine Learning: A Systematic Literature Review. ConcienciaDigital, 7(3.1), 46-67. https://doi.org/10.33262/concienciadigital.v7i3.1.3120
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References
An, D., Choi, J.-H., & Kim, N. H. (2018). Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies. Structural Health Monitoring, 17(5), 1095–1109. https://doi.org/10.1177/1475921717736226
Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3(November), 1–24. https://doi.org/10.3389/frai.2020.578613
Cakir, M., Guvenc, M. A., & Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers and Industrial Engineering, 151, 106948. https://doi.org/10.1016/j.cie.2020.106948
Chen, Y., Peng, G., Zhu, Z., & Li, S. (2020). A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Applied Soft Computing, 86, 105919. https://doi.org/10.1016/j.asoc.2019.105919
Cheng, C., Ding, J., & Zhang, Y. (2020). A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series. Neurocomputing, 406, 204–214. https://doi.org/10.1016/j.neucom.2020.04.005
Cheng, C., Ma, G., Zhang, Y., Sun, M., Teng, F., Ding, H., & Yuan, Y. (2020). A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings. IEEE/ASME Transactions on Mechatronics, 25(3), 1243–1254. https://doi.org/10.1109/TMECH.2020.2971503
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. https://doi.org/10.1016/j.aei.2021.101247
Çinar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (Switzerland), 12(19). https://doi.org/10.3390/su12198211
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298. https://doi.org/10.1016/j.compind.2020.103298
Ding, J., Huang, L., Xiao, D., & Li, X. (2020). GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction. Sensors, 20(7), 1946. https://doi.org/10.3390/s20071946
Huang, G., Li, H., Ou, J., Zhang, Y., & Zhang, M. (2020). A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM. Sensors, 20(7), 1864. https://doi.org/10.3390/s20071864
Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01. School of Computer Science and Mathematics, Keele University. October 2021, 2007.
Li, Xiang, Zhang, W., & Ding, Q. (2019a). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering and System Safety, 182(October 2018), 208–218. https://doi.org/10.1016/j.ress.2018.11.011
Li, Xiang, Zhang, W., & Ding, Q. (2019b). 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, Xiaochuan, Elasha, F., Shanbr, S., & Mba, D. (2019). Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies, 12(14), 2705. https://doi.org/10.3390/en12142705
Liu, R., Yang, B., & Hauptmann, A. G. (2020). Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network. IEEE Transactions on Industrial Informatics, 16(1), 87–96. https://doi.org/10.1109/TII.2019.2915536
Liu, Y., He, B., Liu, F., Lu, S., Zhao, Y., & Zhao, J. (2016). Remaining useful life prediction of rolling bearings using PSR, JADE, and extreme learning machine. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/8623530
Liu, Z., Zuo, M. J., & Qin, Y. (2016). Remaining useful life prediction of rolling element bearings based on health state assessment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(2), 314–330. https://doi.org/10.1177/0954406215590167
Lu, Y.-W., Hsu, C.-Y., & Huang, K.-C. (2020). An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction. Processes, 8(9), 1155. https://doi.org/10.3390/pr8091155
Lu, Y., Li, Q., & Liang, S. Y. (2018). Physics-based intelligent prognosis for rolling bearing with fault feature extraction. The International Journal of Advanced Manufacturing Technology, 97(1–4), 611–620. https://doi.org/10.1007/s00170-018-1959-0
Mao, W., He, J., Tang, J., & Li, Y. (2018). Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Advances in Mechanical Engineering, 10(12), 1–18. https://doi.org/10.1177/1687814018817184
Mao, W., He, J., & Zuo, M. J. (2020). Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning. IEEE Transactions on Instrumentation and Measurement, 69(4), 1594–1608. https://doi.org/10.1109/TIM.2019.2917735
Meng, Z., Li, J., Yin, N., & Pan, Z. (2020). Remaining useful life prediction of rolling bearing using fractal theory. Measurement, 156, 107572. https://doi.org/10.1016/j.measurement.2020.107572
Mushtaq, S., Manjurul Islam, M. M., & Sohaib, M. (2021). Deep learning aided data-driven fault diagnosis of rotatory machine: A comprehensive review. Energies, 14(16). https://doi.org/10.3390/en14165150
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-morello, B., Zerhouni, N., Varnier, C., Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-morello, B., Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests . To cite this version : HAL Id : hal-00719503 PRONOSTIA : An Experimental Platform for Bearings Accelerated Degradation Tests. IEEE International Conference on Prognostics and Health Management, PHM’12., Jun 2012, Denver, Col- Orado, United States., 1–8.
Pan, Z., Meng, Z., Chen, Z., Gao, W., & Shi, Y. (2020). A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings. Mechanical Systems and Signal Processing, 144, 106899. https://doi.org/10.1016/j.ymssp.2020.106899
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56(November 2019), 117–132. https://doi.org/10.1016/j.jmsy.2020.05.013
Rai, A., & Kim, J.-M. (2020). A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings. Measurement, 164, 108002. https://doi.org/10.1016/j.measurement.2020.108002
Rai, A., & Upadhyay, S. H. (2018). An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence. Applied Soft Computing, 71, 36–50. https://doi.org/10.1016/j.asoc.2018.06.038
Ren, L., Sun, Y., Wang, H., & Zhang, L. (2018). Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network. IEEE Access, 6, 13041–13049. https://doi.org/10.1109/ACCESS.2018.2804930
Rohani Bastami, A., Aasi, A., & Arghand, H. A. (2019). Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43(S1), 233–245. https://doi.org/10.1007/s40998-018-0108-y
She, D., Jia, M., & Pecht, M. G. (2020). Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing. Measurement Science and Technology, 31(10), 105005. https://doi.org/10.1088/1361-6501/ab8c0f
Singh, J., Darpe, A. K., & Singh, S. P. (2020). Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and K -means clustering. Measurement Science and Technology, 31(8), 085601. https://doi.org/10.1088/1361-6501/ab6671
Tian, Q., & Wang, H. (2020). An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction. Applied Sciences, 10(1), 346. https://doi.org/10.3390/app10010346
Wang, B., Lei, Y., Li, N., & Li, N. (2020). 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, F., Liu, X., Deng, G., Yu, X., Li, H., & Han, Q. (2019). Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network. Neural Processing Letters, 50(3), 2437–2454. https://doi.org/10.1007/s11063-019-10016-w
Wang, J., Mo, Z., Zhang, H., & Miao, Q. (2019). A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image. IEEE Access, 7, 42373–42383. https://doi.org/10.1109/ACCESS.2019.2907131
Wang, Z., Ma, H., Chen, H., Yan, B., & Chu, X. (2020). Performance degradation assessment of rolling bearing based on convolutional neural network and deep long-short term memory network. International Journal of Production Research, 58(13), 3931–3943. https://doi.org/10.1080/00207543.2019.1636325
Wu, B., Gao, Y., Feng, S., & Chanwimalueang, T. (2018). Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery. Entropy, 20(10), 747. https://doi.org/10.3390/e20100747
Wu, C., Feng, F., Wu, S., Jiang, P., & Wang, J. (2019). A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(11), 526. https://doi.org/10.1007/s40430-019-2010-6
Xu, F., Song, X., Tsui, K.-L., Yang, F., & Huang, Z. (2019). Bearing Performance Degradation Assessment Based on Ensemble Empirical Mode Decomposition and Affinity Propagation Clustering. IEEE Access, 7, 54623–54637. https://doi.org/10.1109/ACCESS.2019.2913186
Yan, M., Wang, X., Wang, B., Chang, M., & Muhammad, I. (2020). Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. ISA Transactions, 98, 471–482. https://doi.org/10.1016/j.isatra.2019.08.058
Zhang, D., Stewart, E., Ye, J., Entezami, M., & Roberts, C. (2020). Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions. IEEE Transactions on Instrumentation and Measurement, 69(6), 2996–3004. https://doi.org/10.1109/TIM.2019.2929669
Zhang, N., Wu, L., Wang, Z., & Guan, Y. (2018). Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions. Entropy, 20(12), 944. https://doi.org/10.3390/e20120944
Zhao, C., Huang, X., Li, Y., & Yousaf Iqbal, M. (2020). A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction. Sensors, 20(24), 7109. https://doi.org/10.3390/s20247109
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
Biggio, L., & Kastanis, I. (2020). Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead. Frontiers in Artificial Intelligence, 3(November), 1–24. https://doi.org/10.3389/frai.2020.578613
Cakir, M., Guvenc, M. A., & Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers and Industrial Engineering, 151, 106948. https://doi.org/10.1016/j.cie.2020.106948
Chen, Y., Peng, G., Zhu, Z., & Li, S. (2020). A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Applied Soft Computing, 86, 105919. https://doi.org/10.1016/j.asoc.2019.105919
Cheng, C., Ding, J., & Zhang, Y. (2020). A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series. Neurocomputing, 406, 204–214. https://doi.org/10.1016/j.neucom.2020.04.005
Cheng, C., Ma, G., Zhang, Y., Sun, M., Teng, F., Ding, H., & Yuan, Y. (2020). A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings. IEEE/ASME Transactions on Mechatronics, 25(3), 1243–1254. https://doi.org/10.1109/TMECH.2020.2971503
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. https://doi.org/10.1016/j.aei.2021.101247
Çinar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (Switzerland), 12(19). https://doi.org/10.3390/su12198211
Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298. https://doi.org/10.1016/j.compind.2020.103298
Ding, J., Huang, L., Xiao, D., & Li, X. (2020). GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction. Sensors, 20(7), 1946. https://doi.org/10.3390/s20071946
Huang, G., Li, H., Ou, J., Zhang, Y., & Zhang, M. (2020). A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM. Sensors, 20(7), 1864. https://doi.org/10.3390/s20071864
Kitchenham, B. A., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01. School of Computer Science and Mathematics, Keele University. October 2021, 2007.
Li, Xiang, Zhang, W., & Ding, Q. (2019a). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering and System Safety, 182(October 2018), 208–218. https://doi.org/10.1016/j.ress.2018.11.011
Li, Xiang, Zhang, W., & Ding, Q. (2019b). 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, Xiaochuan, Elasha, F., Shanbr, S., & Mba, D. (2019). Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning. Energies, 12(14), 2705. https://doi.org/10.3390/en12142705
Liu, R., Yang, B., & Hauptmann, A. G. (2020). Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network. IEEE Transactions on Industrial Informatics, 16(1), 87–96. https://doi.org/10.1109/TII.2019.2915536
Liu, Y., He, B., Liu, F., Lu, S., Zhao, Y., & Zhao, J. (2016). Remaining useful life prediction of rolling bearings using PSR, JADE, and extreme learning machine. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/8623530
Liu, Z., Zuo, M. J., & Qin, Y. (2016). Remaining useful life prediction of rolling element bearings based on health state assessment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(2), 314–330. https://doi.org/10.1177/0954406215590167
Lu, Y.-W., Hsu, C.-Y., & Huang, K.-C. (2020). An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction. Processes, 8(9), 1155. https://doi.org/10.3390/pr8091155
Lu, Y., Li, Q., & Liang, S. Y. (2018). Physics-based intelligent prognosis for rolling bearing with fault feature extraction. The International Journal of Advanced Manufacturing Technology, 97(1–4), 611–620. https://doi.org/10.1007/s00170-018-1959-0
Mao, W., He, J., Tang, J., & Li, Y. (2018). Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Advances in Mechanical Engineering, 10(12), 1–18. https://doi.org/10.1177/1687814018817184
Mao, W., He, J., & Zuo, M. J. (2020). Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning. IEEE Transactions on Instrumentation and Measurement, 69(4), 1594–1608. https://doi.org/10.1109/TIM.2019.2917735
Meng, Z., Li, J., Yin, N., & Pan, Z. (2020). Remaining useful life prediction of rolling bearing using fractal theory. Measurement, 156, 107572. https://doi.org/10.1016/j.measurement.2020.107572
Mushtaq, S., Manjurul Islam, M. M., & Sohaib, M. (2021). Deep learning aided data-driven fault diagnosis of rotatory machine: A comprehensive review. Energies, 14(16). https://doi.org/10.3390/en14165150
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-morello, B., Zerhouni, N., Varnier, C., Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-morello, B., Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests . To cite this version : HAL Id : hal-00719503 PRONOSTIA : An Experimental Platform for Bearings Accelerated Degradation Tests. IEEE International Conference on Prognostics and Health Management, PHM’12., Jun 2012, Denver, Col- Orado, United States., 1–8.
Pan, Z., Meng, Z., Chen, Z., Gao, W., & Shi, Y. (2020). A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings. Mechanical Systems and Signal Processing, 144, 106899. https://doi.org/10.1016/j.ymssp.2020.106899
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56(November 2019), 117–132. https://doi.org/10.1016/j.jmsy.2020.05.013
Rai, A., & Kim, J.-M. (2020). A novel health indicator based on the Lyapunov exponent, a probabilistic self-organizing map, and the Gini-Simpson index for calculating the RUL of bearings. Measurement, 164, 108002. https://doi.org/10.1016/j.measurement.2020.108002
Rai, A., & Upadhyay, S. H. (2018). An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence. Applied Soft Computing, 71, 36–50. https://doi.org/10.1016/j.asoc.2018.06.038
Ren, L., Sun, Y., Wang, H., & Zhang, L. (2018). Prediction of Bearing Remaining Useful Life With Deep Convolution Neural Network. IEEE Access, 6, 13041–13049. https://doi.org/10.1109/ACCESS.2018.2804930
Rohani Bastami, A., Aasi, A., & Arghand, H. A. (2019). Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43(S1), 233–245. https://doi.org/10.1007/s40998-018-0108-y
She, D., Jia, M., & Pecht, M. G. (2020). Sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing. Measurement Science and Technology, 31(10), 105005. https://doi.org/10.1088/1361-6501/ab8c0f
Singh, J., Darpe, A. K., & Singh, S. P. (2020). Bearing remaining useful life estimation using an adaptive data-driven model based on health state change point identification and K -means clustering. Measurement Science and Technology, 31(8), 085601. https://doi.org/10.1088/1361-6501/ab6671
Tian, Q., & Wang, H. (2020). An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction. Applied Sciences, 10(1), 346. https://doi.org/10.3390/app10010346
Wang, B., Lei, Y., Li, N., & Li, N. (2020). 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, F., Liu, X., Deng, G., Yu, X., Li, H., & Han, Q. (2019). Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network. Neural Processing Letters, 50(3), 2437–2454. https://doi.org/10.1007/s11063-019-10016-w
Wang, J., Mo, Z., Zhang, H., & Miao, Q. (2019). A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image. IEEE Access, 7, 42373–42383. https://doi.org/10.1109/ACCESS.2019.2907131
Wang, Z., Ma, H., Chen, H., Yan, B., & Chu, X. (2020). Performance degradation assessment of rolling bearing based on convolutional neural network and deep long-short term memory network. International Journal of Production Research, 58(13), 3931–3943. https://doi.org/10.1080/00207543.2019.1636325
Wu, B., Gao, Y., Feng, S., & Chanwimalueang, T. (2018). Sparse Optimistic Based on Lasso-LSQR and Minimum Entropy De-Convolution with FARIMA for the Remaining Useful Life Prediction of Machinery. Entropy, 20(10), 747. https://doi.org/10.3390/e20100747
Wu, C., Feng, F., Wu, S., Jiang, P., & Wang, J. (2019). A method for constructing rolling bearing lifetime health indicator based on multi-scale convolutional neural networks. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(11), 526. https://doi.org/10.1007/s40430-019-2010-6
Xu, F., Song, X., Tsui, K.-L., Yang, F., & Huang, Z. (2019). Bearing Performance Degradation Assessment Based on Ensemble Empirical Mode Decomposition and Affinity Propagation Clustering. IEEE Access, 7, 54623–54637. https://doi.org/10.1109/ACCESS.2019.2913186
Yan, M., Wang, X., Wang, B., Chang, M., & Muhammad, I. (2020). Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. ISA Transactions, 98, 471–482. https://doi.org/10.1016/j.isatra.2019.08.058
Zhang, D., Stewart, E., Ye, J., Entezami, M., & Roberts, C. (2020). Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions. IEEE Transactions on Instrumentation and Measurement, 69(6), 2996–3004. https://doi.org/10.1109/TIM.2019.2929669
Zhang, N., Wu, L., Wang, Z., & Guan, Y. (2018). Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions. Entropy, 20(12), 944. https://doi.org/10.3390/e20120944
Zhao, C., Huang, X., Li, Y., & Yousaf Iqbal, M. (2020). A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction. Sensors, 20(24), 7109. https://doi.org/10.3390/s20247109
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