RUL Estimation of Rolling Element Bearings Using a Hybrid Wavelet Packet Decomposition–Recursive Feature Elimination–Adaptive Neuro Fuzzy Inference System Framework
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Published
Feb 25, 2026
Abdel Wahhab Lourari
Tarak Benhedjouh
Abstract
Rolling element bearings are critical components in rotating machinery, and their unexpected failures can cause severe downtime and economic losses. Therefore, accurate estimation of remaining useful life (RUL) is essential to ensure system reliability and enable predictive maintenance strategies. This paper presents a novel hybrid framework that integrates Wavelet Packet Decomposition (WPD), Recursive Feature Elimination (RFE), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent RUL estimation of bearings. First, vibration signals from the well-known IMS dataset are acquired and decomposed using WPD to capture multi-resolution information. A comprehensive set of health indicators is then computed from each decomposition level, reflecting the degradation dynamics of bearings. To reduce redundancy and enhance discriminative power, the most relevant features are selected using the RFE algorithm. Finally, the refined features are fed into an ANFIS model to estimate the RUL. Comparative analyses with multiple Artificial Neural Network (ANN) based models are conducted to assess the effectiveness of the
proposed approach. Experimental results demonstrate that the hybrid WPD–RFE–ANFIS framework achieves outstanding predictive performance, reaching an accuracy of 99.98%, thereby outperforming traditional ANN architectures. This study highlights the potential of hybrid intelligent models for advancing prognostics and health management (PHM) in industrial applications.
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Keywords
Wavelet Packet Decomposition (WPD), fault Prognosis, Recursive Feature Elimination (RFE), Adaptive Neuro Fuzzy Inference System (ANFIS), Feature extraction, Feature selection
References
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Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172.
Alomari, Y., Andó, M., & Baptista, M. L. (2023). Advancing aircraft engine rul predictions: an interpretable integrated approach of feature engineering and aggregated feature importance. Scientific Reports, 13(1), 13466.
Antoni, J. (2016). The infogram: Entropic evidence of the signature of repetitive transients. Mechanical Systems and Signal Processing, 74, 73–94.
Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., & Deliou, A. (2017). A new time–frequency method for identification and classification of ball bearing faults. Journal of Sound and Vibration, 397, 241–265.
Belmiloud, D., Benkedjouh, T., Lachi, M., Laggoun, A., & Dron, J. (2018). Deep convolutional neural networks for bearings failure predictionand temperature correlation. Journal of Vibroengineering, 20(8), 2878–2891.
Bono, F., Cinquemani, S., Chatterton, S., & Pennacchi, P. (2022). A deep learning approach for fault detection and rul estimation in bearings. In Nde 4.0, predictive maintenance, and communication and energy systems in a globally networked world (Vol. 12049, pp. 71–83).
Buchaiah, S., & Shakya, P. (2022). Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection. Measurement, 188, 110506.
Chen, B., Song, D., Cheng, Y., Zhang, W., Huang, B., & Muhamedsalih, Y. (2022). Igigram: An improved gini index-based envelope analysis for rolling bearing fault diagnosis. Journal of Dynamics, Monitoring and Diagnostics, 111–124.
Cheng, W.-N., Cheng, C.-C., Lei, Y.-H., & Tsai, P.-C. (2020). Feature selection for predicting tool wear of machine tools. The International Journal of Advanced Manufacturing Technology, 111, 1483–1501.
Du, W., & Wang, Y. (2019). Stacked convolutional lstm models for prognosis of bearing performance degradation. In 2019 prognostics and system health management conference (phm-qingdao) (pp. 1–6).
Duan, C., Shen, Y., Guo, K., Sheng, B., & Wang, Y. (2023). Remaining useful life prediction based on a pca and similarity methods. Measurement Science and Technology, 35(3), 035020.
Dwyer, R. (1983). Detection of non-gaussian signals by frequency domain kurtosis estimation. In Icassp’83. ieee international conference on acoustics, speech, and signal processing (Vol. 8, pp. 607–610).
Eker, O. F., Camci, F., & Jennions, I. K. (2012). Major challenges in prognostics: Study on benchmarking prognostics datasets. In Phm society european conference (Vol. 1).
El Yousfi, B., Lourari, A.W., Bouzar Essaidi, A., Soualhi, A., & Medjaher, K. (2025). A probabilistic physics-guided framework for crack propagation prognostic. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 239(16), 6530–6543.
Ghods, A., & Lee, H.-H. (2016). Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing, 188, 206–216.
Gini, C. (1921). Measurement of inequality of incomes. The economic journal, 31(121), 124–125.
Gohari, M., Tahmasebi, M., & Ghorbani, M. (2023). Design a condition monitoring system for rotating machinery gearboxes by oil quality measurements and vibration analyses. Control Systems and Optimization Letters, 1(2), 64–68.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
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.
Gupta, M., Wadhvani, R., & Rasool, A. (2023). A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-Based Systems, 259, 110070.
Habbouche, H., Benkedjouh, T., & Zerhouni, N. (2021). Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition. The International Journal of Advanced Manufacturing Technology, 114(1-2), 145–157.
He, M., Zhou, Y., Li, Y., Wu, G., & Tang, G. (2020). Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation. Measurement, 156, 107582.
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical systems and signal processing, 23(3), 724–739.
Hong, S., Duan, X., Peng, Y., Liu, H., & Zio, E. (2023). Bearing degradation prediction by wpd and dpnn: Introducing a novel deep learning method. IEEE Systems, Man, and Cybernetics Magazine, 9(1), 18–24. Hoyer, P. O. (2004). Non-negative matrix factorization with sparseness constraints. Journal of machine learning research, 5(9).
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical systems and signal processing, 72, 303–315.
Kang, Z., Catal, C., & Tekinerdogan, B. (2021). Remaining useful life (rul) prediction of equipment in production lines using artificial neural networks. Sensors, 21(3), 932.
Kumar, A., Parkash, C., Tang, H., & Xiang, J. (2023). Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (rul) of bearing. Advanced Engineering Informatics, 58, 102206.
Kumar, P. S., Kumaraswamidhas, L., & Laha, S. (2021). Selection of efficient degradation features for rolling element bearing prognosis using gaussian process regression method. ISA transactions, 112, 386–401.
Lai, J.-J., Pan, S.-J., Ong, C.-W., & Chen, C.-L. (2022). Weibull reliability regression model for prediction of bearing remaining useful life. In Computer-aided chemical engineering (Vol. 49, pp. 829–834). Elsevier.
Lan, X., Li, Y., Su, Y., Meng, L., Kong, X., & Xu, T. (2022). Performance degradation prediction model of rolling bearing based on self-checking long short-term memory network. Measurement Science and Technology, 34(1), 015016.
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.
Lei, Y., Lin, J., Zuo, M. J., & He, Z. (2014). Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, 48, 292–305.
Li, W., & Deng, L. (2023). A hybrid model-based prognostics approach for estimating remaining useful life of rolling bearings. Measurement Science and Technology, 34(10), 105012.
Liu, J., Wang, W., & Golnaraghi, F. (2009). An enhanced diagnostic scheme for bearing condition monitoring. IEEE Transactions on Instrumentation and Measurement, 59(2), 309–321.
Liu, Q., Zhang, Y., Si, X., & Fan, Z. (2023). Dlvr-nwp: A novel data-driven bearing degradation model for rul estimation. IEEE Transactions on Instrumentation and Measurement, 72, 1–9.
Lourari, A. W., BENKEDJOUH, T., & El Yousfi, B. (2024). Advanced prognostic models for bearing health: a comparative analysis of bilstm and anfis. Electrotehnica, Electronica, Automatica, 72(2).
Lourari, A. W., El Yousfi, B., Benkedjouh, T., Bouzar Essaidi, A., & Soualhi, A. (2024). Enhancing bearing and gear fault diagnosis: A vmd-pso approach with multisensory signal integration. Journal of Vibration and Control, 10775463241273842.
Lourari, A. w., Soualhi, A., & Benkedjouh, T. (2024). Advancing bearing fault diagnosis under variable working conditions: a ceemdan-sbs approach with vibroelectric signal integration. The International Journal of Advanced Manufacturing Technology, 132(5), 2753–2772.
Lourari, A. w., Soualhi, A., Medjaher, K., & Benkedjouh, T. (2024). New health indicators for the monitoring of bearing failures under variable loads. Structural Health Monitoring, 23(5), 2922–2941.
Lourari, A. W., Yousfi, B. E., & Essaidi, A. B. (2025). Enhanced diagnosis of bearing and gear faults using hilbert-huang transform, singular value decomposition, and supervised learning methods. The International Journal of Advanced Manufacturing Technology, 1–17.
Lu, G., Wen, X., He, G., Yi, X., & Yan, P. (2021). Early fault warning and identification in condition monitoring of bearing via wavelet packet decomposition coupled with graph. IEEE/ASME Transactions on Mechatronics, 27(5), 3155–3164.
Lv, Y., Zhao, W., Zhao, Z., Li, W., & Ng, K. K. (2022). Vibration signal-based early fault prognosis: Status quo and applications. Advanced Engineering Informatics, 52, 101609.
Meddour, I., Messekher, S. E., Younes, R., & Yallese, M. A. (2021). Selection of bearing health indicator by gra for anfis-based forecasting of remaining useful life. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(3), 144.
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.
Medjaher, K., Zerhouni, N., & Gouriveau, R. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
Motahari-Nezhad, M., & Jafari, S. M. (2020). Anfis system for prognosis of dynamometer high-speed ball bearing based on frequency domain acoustic emission signals. Measurement, 166, 108154.
Peter, W. T., &Wang, D. (2013). The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “two automatic vibration-based fault diagnostic methods using the novel sparsity measurement–parts 1 and 2”. Mechanical Systems and Signal Processing, 40(2), 499–519.
Rathore, M. S., & Harsha, S. (2022). Prognostic analysis of high-speed cylindrical roller bearing using weibull distribution and k-nearest neighbor. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(1), 011005.
Sekini, M., El Yousfi, B., Benkedjouh, T., Medjaher, K., & Lourari, A. W. (2025). Angular resampling-sparse representation classification (ar-src): A new method for bearing fault diagnosis in non-stationary conditions. Journal of Vibration and Control, 10775463251408382.
Shen, F., & Yan, R. (2021). A new intermediate-domain svm-based transfer model for rolling bearing rul prediction. IEEE/ASME Transactions on Mechatronics, 27(3), 1357–1369.
Soualhi, A., Lamraoui, M., Elyousfi, B., & Razik, H. (2022). Phm survey: Implementation of prognostic methods for monitoring industrial systems. Energies, 15(19), 6909.
Soualhi, A., Yousfi, B. E., Lamraoui, M., & Medjaher, K. (2022). Application of the prognostic and health management to industrial systems. In International conference on artificial intelligence in renewable energetic systems (pp. 652–664).
Thoppil, N. M., Vasu, V., & Rao, C. (2021). Deep learning algorithms for machinery health prognostics using time-series data: A review. Journal of Vibration Engineering & Technologies, 1–23.
Tran, N. T., Trieu, H. T., Tran, V. T., Ngo, H. H., & Dao, Q. K. (2021). An overview of the application of machine learning in predictive maintenance. Petrovietnam Journal, 10, 47–61.
wahhab LOURARI, A., & BENKEDJOUH, T. (2026). A robust ai-driven multisensory framework for bearing and gear fault diagnosis based on vmd, hes, and rfe. 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).
Wang, J., & Liao, X. (2005). Advances in neural networksisnn 2005: Second international symposium on neural networks, chongqing, china, may 30-june 1, 2005, proceedings (Vol. 3). Springer Science & Business Media.
Wang, Y., Xu, G., Zhang, Q., Liu, D., & Jiang, K. (2015). Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. Journal of Sound and Vibration, 348, 381–396.
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.
Wang, Y., Zhao, Y., & Addepalli, S. (2020). Remaining useful life prediction using deep learning approaches: A review. Procedia manufacturing, 49, 81–88.
Widodo, A., & Yang, B.-S. (2011). Application of relevance vector machine and survival probability to machine degradation assessment. Expert Systems with Applications, 38(3), 2592–2599.
Wu, B., Li, W., Qiu, M.-q., et al. (2017). Remaining useful life prediction of bearing with vibration signals based on a novel indicator. Shock and Vibration, 2017.
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.
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Ali, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56, 150–172.
Alomari, Y., Andó, M., & Baptista, M. L. (2023). Advancing aircraft engine rul predictions: an interpretable integrated approach of feature engineering and aggregated feature importance. Scientific Reports, 13(1), 13466.
Antoni, J. (2016). The infogram: Entropic evidence of the signature of repetitive transients. Mechanical Systems and Signal Processing, 74, 73–94.
Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., & Deliou, A. (2017). A new time–frequency method for identification and classification of ball bearing faults. Journal of Sound and Vibration, 397, 241–265.
Belmiloud, D., Benkedjouh, T., Lachi, M., Laggoun, A., & Dron, J. (2018). Deep convolutional neural networks for bearings failure predictionand temperature correlation. Journal of Vibroengineering, 20(8), 2878–2891.
Bono, F., Cinquemani, S., Chatterton, S., & Pennacchi, P. (2022). A deep learning approach for fault detection and rul estimation in bearings. In Nde 4.0, predictive maintenance, and communication and energy systems in a globally networked world (Vol. 12049, pp. 71–83).
Buchaiah, S., & Shakya, P. (2022). Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection. Measurement, 188, 110506.
Chen, B., Song, D., Cheng, Y., Zhang, W., Huang, B., & Muhamedsalih, Y. (2022). Igigram: An improved gini index-based envelope analysis for rolling bearing fault diagnosis. Journal of Dynamics, Monitoring and Diagnostics, 111–124.
Cheng, W.-N., Cheng, C.-C., Lei, Y.-H., & Tsai, P.-C. (2020). Feature selection for predicting tool wear of machine tools. The International Journal of Advanced Manufacturing Technology, 111, 1483–1501.
Du, W., & Wang, Y. (2019). Stacked convolutional lstm models for prognosis of bearing performance degradation. In 2019 prognostics and system health management conference (phm-qingdao) (pp. 1–6).
Duan, C., Shen, Y., Guo, K., Sheng, B., & Wang, Y. (2023). Remaining useful life prediction based on a pca and similarity methods. Measurement Science and Technology, 35(3), 035020.
Dwyer, R. (1983). Detection of non-gaussian signals by frequency domain kurtosis estimation. In Icassp’83. ieee international conference on acoustics, speech, and signal processing (Vol. 8, pp. 607–610).
Eker, O. F., Camci, F., & Jennions, I. K. (2012). Major challenges in prognostics: Study on benchmarking prognostics datasets. In Phm society european conference (Vol. 1).
El Yousfi, B., Lourari, A.W., Bouzar Essaidi, A., Soualhi, A., & Medjaher, K. (2025). A probabilistic physics-guided framework for crack propagation prognostic. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 239(16), 6530–6543.
Ghods, A., & Lee, H.-H. (2016). Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing, 188, 206–216.
Gini, C. (1921). Measurement of inequality of incomes. The economic journal, 31(121), 124–125.
Gohari, M., Tahmasebi, M., & Ghorbani, M. (2023). Design a condition monitoring system for rotating machinery gearboxes by oil quality measurements and vibration analyses. Control Systems and Optimization Letters, 1(2), 64–68.
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
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.
Gupta, M., Wadhvani, R., & Rasool, A. (2023). A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network. Knowledge-Based Systems, 259, 110070.
Habbouche, H., Benkedjouh, T., & Zerhouni, N. (2021). Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition. The International Journal of Advanced Manufacturing Technology, 114(1-2), 145–157.
He, M., Zhou, Y., Li, Y., Wu, G., & Tang, G. (2020). Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation. Measurement, 156, 107582.
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical systems and signal processing, 23(3), 724–739.
Hong, S., Duan, X., Peng, Y., Liu, H., & Zio, E. (2023). Bearing degradation prediction by wpd and dpnn: Introducing a novel deep learning method. IEEE Systems, Man, and Cybernetics Magazine, 9(1), 18–24. Hoyer, P. O. (2004). Non-negative matrix factorization with sparseness constraints. Journal of machine learning research, 5(9).
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical systems and signal processing, 72, 303–315.
Kang, Z., Catal, C., & Tekinerdogan, B. (2021). Remaining useful life (rul) prediction of equipment in production lines using artificial neural networks. Sensors, 21(3), 932.
Kumar, A., Parkash, C., Tang, H., & Xiang, J. (2023). Intelligent framework for degradation monitoring, defect identification and estimation of remaining useful life (rul) of bearing. Advanced Engineering Informatics, 58, 102206.
Kumar, P. S., Kumaraswamidhas, L., & Laha, S. (2021). Selection of efficient degradation features for rolling element bearing prognosis using gaussian process regression method. ISA transactions, 112, 386–401.
Lai, J.-J., Pan, S.-J., Ong, C.-W., & Chen, C.-L. (2022). Weibull reliability regression model for prediction of bearing remaining useful life. In Computer-aided chemical engineering (Vol. 49, pp. 829–834). Elsevier.
Lan, X., Li, Y., Su, Y., Meng, L., Kong, X., & Xu, T. (2022). Performance degradation prediction model of rolling bearing based on self-checking long short-term memory network. Measurement Science and Technology, 34(1), 015016.
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.
Lei, Y., Lin, J., Zuo, M. J., & He, Z. (2014). Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, 48, 292–305.
Li, W., & Deng, L. (2023). A hybrid model-based prognostics approach for estimating remaining useful life of rolling bearings. Measurement Science and Technology, 34(10), 105012.
Liu, J., Wang, W., & Golnaraghi, F. (2009). An enhanced diagnostic scheme for bearing condition monitoring. IEEE Transactions on Instrumentation and Measurement, 59(2), 309–321.
Liu, Q., Zhang, Y., Si, X., & Fan, Z. (2023). Dlvr-nwp: A novel data-driven bearing degradation model for rul estimation. IEEE Transactions on Instrumentation and Measurement, 72, 1–9.
Lourari, A. W., BENKEDJOUH, T., & El Yousfi, B. (2024). Advanced prognostic models for bearing health: a comparative analysis of bilstm and anfis. Electrotehnica, Electronica, Automatica, 72(2).
Lourari, A. W., El Yousfi, B., Benkedjouh, T., Bouzar Essaidi, A., & Soualhi, A. (2024). Enhancing bearing and gear fault diagnosis: A vmd-pso approach with multisensory signal integration. Journal of Vibration and Control, 10775463241273842.
Lourari, A. w., Soualhi, A., & Benkedjouh, T. (2024). Advancing bearing fault diagnosis under variable working conditions: a ceemdan-sbs approach with vibroelectric signal integration. The International Journal of Advanced Manufacturing Technology, 132(5), 2753–2772.
Lourari, A. w., Soualhi, A., Medjaher, K., & Benkedjouh, T. (2024). New health indicators for the monitoring of bearing failures under variable loads. Structural Health Monitoring, 23(5), 2922–2941.
Lourari, A. W., Yousfi, B. E., & Essaidi, A. B. (2025). Enhanced diagnosis of bearing and gear faults using hilbert-huang transform, singular value decomposition, and supervised learning methods. The International Journal of Advanced Manufacturing Technology, 1–17.
Lu, G., Wen, X., He, G., Yi, X., & Yan, P. (2021). Early fault warning and identification in condition monitoring of bearing via wavelet packet decomposition coupled with graph. IEEE/ASME Transactions on Mechatronics, 27(5), 3155–3164.
Lv, Y., Zhao, W., Zhao, Z., Li, W., & Ng, K. K. (2022). Vibration signal-based early fault prognosis: Status quo and applications. Advanced Engineering Informatics, 52, 101609.
Meddour, I., Messekher, S. E., Younes, R., & Yallese, M. A. (2021). Selection of bearing health indicator by gra for anfis-based forecasting of remaining useful life. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43(3), 144.
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.
Medjaher, K., Zerhouni, N., & Gouriveau, R. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
Motahari-Nezhad, M., & Jafari, S. M. (2020). Anfis system for prognosis of dynamometer high-speed ball bearing based on frequency domain acoustic emission signals. Measurement, 166, 108154.
Peter, W. T., &Wang, D. (2013). The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “two automatic vibration-based fault diagnostic methods using the novel sparsity measurement–parts 1 and 2”. Mechanical Systems and Signal Processing, 40(2), 499–519.
Rathore, M. S., & Harsha, S. (2022). Prognostic analysis of high-speed cylindrical roller bearing using weibull distribution and k-nearest neighbor. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5(1), 011005.
Sekini, M., El Yousfi, B., Benkedjouh, T., Medjaher, K., & Lourari, A. W. (2025). Angular resampling-sparse representation classification (ar-src): A new method for bearing fault diagnosis in non-stationary conditions. Journal of Vibration and Control, 10775463251408382.
Shen, F., & Yan, R. (2021). A new intermediate-domain svm-based transfer model for rolling bearing rul prediction. IEEE/ASME Transactions on Mechatronics, 27(3), 1357–1369.
Soualhi, A., Lamraoui, M., Elyousfi, B., & Razik, H. (2022). Phm survey: Implementation of prognostic methods for monitoring industrial systems. Energies, 15(19), 6909.
Soualhi, A., Yousfi, B. E., Lamraoui, M., & Medjaher, K. (2022). Application of the prognostic and health management to industrial systems. In International conference on artificial intelligence in renewable energetic systems (pp. 652–664).
Thoppil, N. M., Vasu, V., & Rao, C. (2021). Deep learning algorithms for machinery health prognostics using time-series data: A review. Journal of Vibration Engineering & Technologies, 1–23.
Tran, N. T., Trieu, H. T., Tran, V. T., Ngo, H. H., & Dao, Q. K. (2021). An overview of the application of machine learning in predictive maintenance. Petrovietnam Journal, 10, 47–61.
wahhab LOURARI, A., & BENKEDJOUH, T. (2026). A robust ai-driven multisensory framework for bearing and gear fault diagnosis based on vmd, hes, and rfe. 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).
Wang, J., & Liao, X. (2005). Advances in neural networksisnn 2005: Second international symposium on neural networks, chongqing, china, may 30-june 1, 2005, proceedings (Vol. 3). Springer Science & Business Media.
Wang, Y., Xu, G., Zhang, Q., Liu, D., & Jiang, K. (2015). Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. Journal of Sound and Vibration, 348, 381–396.
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.
Wang, Y., Zhao, Y., & Addepalli, S. (2020). Remaining useful life prediction using deep learning approaches: A review. Procedia manufacturing, 49, 81–88.
Widodo, A., & Yang, B.-S. (2011). Application of relevance vector machine and survival probability to machine degradation assessment. Expert Systems with Applications, 38(3), 2592–2599.
Wu, B., Li, W., Qiu, M.-q., et al. (2017). Remaining useful life prediction of bearing with vibration signals based on a novel indicator. Shock and Vibration, 2017.
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.
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Section
Technical Papers