An ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Jan 17, 2024
Abdel wahhab Lourari
Tarak Benkedjouh
Bilal El Yousfi
Abdenour Soualhi
Abstract
Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a novel methodology for bearing prognostics, employing Wavelet Packet Decomposition (WPD) for data preprocessing, Sequential Backward Selection (SBS) for feature selection, and Adaptive Neuro-Fuzzy Inference System (ANFIS) networks for prognostic modeling. The proposed approach consists of two key steps. Firstly, the data undergoes preprocessing through Wavelet Packet Decomposition, enhancing the quality and extracting relevant features. Subsequently, the Remaining Useful Life (RUL) of the bearing is predicted using a degradation model. The accuracy of the proposed method is evaluated using a bearing life dataset obtained from a run-to-failure test (IMS dataset). The results demonstrate the remarkable capability of the ANFIS model to learn and accurately estimate the system’s RUL. By leveraging the combined power of WPD, SBS, and ANFIS, this methodology showcases its potential as an effective prognostic tool for bearing health assessment and proactive maintenance planning.
##plugins.themes.bootstrap3.article.details##
Keywords
Wavelet Packet Decomposition (WPD), fault Prognosis, Sequential Backward Selection (SBS), Adaptive Neuro Fuzzy Inference System (ANFIS), Data preprocessing, Maintenance
References
Abdenour, S., Kamal, M., Franc¸ois, G., et al. (2022). A diagnosis scheme of gearbox faults based on machine learning and motor current analysis. In 2022 prognostics and health management conference (phm-2022 london) (pp. 218–223).
Ahmad, W., Khan, S. A., & Kim, J.-M. (2017). A hybrid prognostics technique for rolling element bearings using adaptive predictive models. IEEE Transactions on Industrial Electronics, 65(2), 1577–1584.
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.
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.
Buchaiah, S., & Shakya, P. (2022). Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection. Measurement, 188, 110506.
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.
Dastourian, B., Dastourian, E., Dastourian, S., & Mahnaie, O. (2014). Discrete wavelet transforms of haars wavelet. International Journal of Science and Technological Research, 3(9), 247–251.
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned vmd and cnn scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Expert Systems with Applications, 167, 114094.
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).
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).
Elforjani, M., & Shanbr, S. (2017). Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Transactions on industrial electronics, 65(7), 5864–5871.
Fu, S., Liu, K., Xu, Y., & Liu, Y. (2016). Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy-means clustering. Shock and Vibration, 2016.
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.
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.
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.
Hamid, M. A., Ibrahim, R. A., Abdelgeliel, M., & Desouki, H. (2023). Bearing fault identification for high-speed wind turbines using cnn. In 2023 11th international conference on smart grid (icsmartgrid) (pp. 1–5).
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.
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.
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.
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.
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.
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, 14759217231219486.
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.
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.
Niu, G., Liu, E., Wang, X., & Zhang, B. (2023). A hybrid bearing prognostic method with fault diagnosis and model fusion. IEEE Transactions on Industrial Informatics.
Sarih, H., Tchangani, A. P., Medjaher, K., & Péré, E. (2019). Data preparation and preprocessing for broadcast systems monitoring in phm framework. In 2019 6th international conference on control, decision and information technologies (codit) (pp. 1444–1449).
Satish, B.,&Sarma, N. (2005). A fuzzy bp approach for diagnosis and prognosis of bearing faults in induction motors. In Ieee power engineering society general meeting, 2005 (pp. 2291–2294).
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and health management, 1(1), 4–23.
Schoen, R. R., Habetler, T. G., Kamran, F., & Bartfield, R. (1995). Motor bearing damage detection using stator current monitoring. IEEE transactions on industry applications, 31(6), 1274–1279.
Siswipraptini, P. C., Aziza, R. N., Sangadji, I., & Indrianto, I. (2020). The design of a smart home controller based on adaline.
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.
Tobon-Mejia, D., Medjaher, K., Zerhouni, N., & Tripot, G. (2011). Estimation of the remaining useful life by using wavelet packet decomposition and hmms. In 2011 aerospace conference (pp. 1–10).
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.
Wang, J., & Liao, X. (2005). Advances in neural networks-isnn 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, 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.
Zhang, B., Zhang, S., & Li, W. (2019). Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry, 106, 14–29.
Zhou, K., & Tang, J. (2023). A wavelet neural network informed by time-domain signal preprocessing for bearing remaining useful life prediction. Applied Mathematical Modelling.
Zhou, L., Duan, F., Mba, D., Wang, W., & Ojolo, S. (2018). Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a ch-46e helicopter aft gearbox. Engineering Failure Analysis, 92, 71–83.
Ahmad, W., Khan, S. A., & Kim, J.-M. (2017). A hybrid prognostics technique for rolling element bearings using adaptive predictive models. IEEE Transactions on Industrial Electronics, 65(2), 1577–1584.
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.
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.
Buchaiah, S., & Shakya, P. (2022). Bearing fault diagnosis and prognosis using data fusion based feature extraction and feature selection. Measurement, 188, 110506.
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.
Dastourian, B., Dastourian, E., Dastourian, S., & Mahnaie, O. (2014). Discrete wavelet transforms of haars wavelet. International Journal of Science and Technological Research, 3(9), 247–251.
Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned vmd and cnn scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Expert Systems with Applications, 167, 114094.
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).
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).
Elforjani, M., & Shanbr, S. (2017). Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Transactions on industrial electronics, 65(7), 5864–5871.
Fu, S., Liu, K., Xu, Y., & Liu, Y. (2016). Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy-means clustering. Shock and Vibration, 2016.
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.
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.
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.
Hamid, M. A., Ibrahim, R. A., Abdelgeliel, M., & Desouki, H. (2023). Bearing fault identification for high-speed wind turbines using cnn. In 2023 11th international conference on smart grid (icsmartgrid) (pp. 1–5).
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.
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.
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.
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.
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.
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, 14759217231219486.
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.
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.
Niu, G., Liu, E., Wang, X., & Zhang, B. (2023). A hybrid bearing prognostic method with fault diagnosis and model fusion. IEEE Transactions on Industrial Informatics.
Sarih, H., Tchangani, A. P., Medjaher, K., & Péré, E. (2019). Data preparation and preprocessing for broadcast systems monitoring in phm framework. In 2019 6th international conference on control, decision and information technologies (codit) (pp. 1444–1449).
Satish, B.,&Sarma, N. (2005). A fuzzy bp approach for diagnosis and prognosis of bearing faults in induction motors. In Ieee power engineering society general meeting, 2005 (pp. 2291–2294).
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and health management, 1(1), 4–23.
Schoen, R. R., Habetler, T. G., Kamran, F., & Bartfield, R. (1995). Motor bearing damage detection using stator current monitoring. IEEE transactions on industry applications, 31(6), 1274–1279.
Siswipraptini, P. C., Aziza, R. N., Sangadji, I., & Indrianto, I. (2020). The design of a smart home controller based on adaline.
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.
Tobon-Mejia, D., Medjaher, K., Zerhouni, N., & Tripot, G. (2011). Estimation of the remaining useful life by using wavelet packet decomposition and hmms. In 2011 aerospace conference (pp. 1–10).
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.
Wang, J., & Liao, X. (2005). Advances in neural networks-isnn 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, 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.
Zhang, B., Zhang, S., & Li, W. (2019). Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry, 106, 14–29.
Zhou, K., & Tang, J. (2023). A wavelet neural network informed by time-domain signal preprocessing for bearing remaining useful life prediction. Applied Mathematical Modelling.
Zhou, L., Duan, F., Mba, D., Wang, W., & Ojolo, S. (2018). Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a ch-46e helicopter aft gearbox. Engineering Failure Analysis, 92, 71–83.
Section
Technical Papers