Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions
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Published
Aug 24, 2023
Moncef Soualhi
Abdenour Soualhi
Khanh T. P. Nguyen
Kamal Medjaher
Guy Clerc Hubert Razik
https://orcid.org/0000-0001-6687-9140
Khanh T. P. Nguyen
Kamal Medjaher
Guy Clerc Hubert Razik
Abstract
Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.
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Keywords
Prognostics and health management, Condition monitoring, Open data science, Data processing, Health indicator, Fault detection and diagnostics, Eelectrical machines, Electrical machines, Rotating machines, Mechanical faults, Electrical faults
References
Adel, A., Hand, O., Fawzi, G., Walid, T., Chemseddine, R., & Djamel, B. (2022). Gear fault detection, identification and classification using mlp neural network. In Recent advances in structural health monitoring and engineering structures: Select proceedings of shm and es 2022 (pp. 221–234). Springer.
Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27.
Benaggoune, K., Yue, M., Jemei, S., & Zerhouni, N. (2022). A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell. Applied Energy, 313, 118835. doi: https://doi.org/10.1016/j.apenergy.2022.118835
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2017). Prognostics and health management for an overhead contact line system-a review. International Journal of Prognostics and Health Management, 8(3).
Breuneval, R., Clerc, G., Nahid-Mobarakeh, B., & Mansouri, B. (2018). Classification with automatic detection of unknown classes based on svm and fuzzy mbf: Application to motor diagnosis. AIMS Electronics and Electrical Engineering, 2(3), 59–84.
Caballé, N. C., Castro, I. T., Pérez, C. J., & Lanza-Gutiérrez, J. M. (2015). A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety, 134, 98–109.
Campbell, J. D., Jardine, A. K., & McGlynn, J. (2016). Asset management excellence: optimizing equipment life-cycle decisions. CRC Press.
Casimir, R., Boutleux, E., Clerc, G., & Yahoui, A. (2006). The use of features selection and nearest neighbors rule for faults diagnostic in induction motors. Engineering Applications of Artificial Intelligence, 19(2), 169–177.
Chen, Y., Peng, G., Xie, C., Zhang, W., Li, C., & Liu, S. (2018). Acdin: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis. Neurocomputing, 294, 61–71.
Chiachío, J., Chiachío, M., Sankararaman, S., Saxena, A., & Goebel, K. (2015). Condition-based prediction of time-dependent reliability in composites. Reliability Engineering & System Safety, 142, 134–147.
Cuguero-Escofet, M. A., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1–12.
de Jonge, B., Teunter, R., & Tinga, T. (2017). The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. Reliability engineering & system safety, 158, 21–30.
de Pater, I., & Mitici, M. (2021). Predictive maintenance for multi-component systems of repairables with remaining-useful-life prognostics and a limited stock of spare components. Reliability Engineering & System Safety, 214, 107761.
Goel, A. K., Singh, G., & Naikan, V. N. A. (2022). A methodology for selection of condition monitoring techniques for rotating machinery. International Journal of Prognostics and Health Management, 13(2).
Golafshan, R., & Sanliturk, K. Y. (2016). Svd and hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults. Mechanical Systems and Signal Processing, 70, 36–50.
Gougam, F., Rahmoune, C., Benazzouz, D., & Merainani, B. (2019, sep). Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions. Journal of Vibroengineering, 21(6), 1636–1650. Retrieved from https://doi.org/10.21595/jve.2019.20092 doi: 10.21595/jve.2019.20092
Gougam, F., Rahmoune, C., Benazzouz, D., Varnier, C., & Nicod, J.-M. (2020). Health monitoring approach of bearing: Application of adaptive neuro fuzzy inference system (anfis) for rul-estimation and autogram analysis for fault-localization. In 2020 prognostics and health management conference (phm-besanc¸on) (p. 200-206). doi: 10.1109/PHM-Besancon49106.2020.00040
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
Jiang, G., Jia, C., Nie, S., Wu, X., He, Q., & Xie, P. (2022). Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals. Measurement, 196, 111159.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328–32338.
Kim, N.-H., An, D., & Choi, J.-H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia cirp, 38, 3–7.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314–334.
Liu, L., Wang, S., Liu, D., Zhang, Y., & Peng, Y. (2015). Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability, 55(9-10), 2092–2096.
Lo, N. G., Soualhi, A., Frin`ı, M., & Razik, H. (2018). Gear and bearings fault detection using motor current signature analysis. In 2018 13th ieee conference on industrial electronics and applications (iciea) (pp. 900–905).
Magnus, K., & Jing. (2019). Vibration-based condition monitoring of heavy duty machine driveline parts: Torque converter, gearbox, axles and bearings. International Journal of Prognostics and Health Management, 10(2).
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012, June). PRONOSTIA : An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management, PHM’12. (Vol. sur CD ROM, pp. 1–8). Denver, Colorado, United States: IEEE Catalog Number: CPF12PHM-CDR.
Nguyen, K. T. P., Medjaher, K., & Tran, D. T. (2022, September). A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artificial Intelligence Review. doi: 10.1007/s10462-022-10260-y
Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., & Zerhouni, N. (2021). Towards an adapted phm approach: Data quality requirements methodology for fault detection applications. Computers in industry, 127, 103414.
Ondel, O., Blanco, E., & Clerc, G. (2007). Beyond the diagnosis: the forecast of state system application in an induction machine. In 2007 ieee international symposium on diagnostics for electric machines, power electronics and drives (pp. 491–496).
Ondel, O., Boutleux, E., & Clerc, G. (2005). Adaptive diagnosis by pattern recognition: Application on an induction machine. In 2005 5th ieee international symposium on diagnostics for electric machines, power electronics and drives (pp. 1–7).
Ooijevaar, T., Pichler, K., Di, Y., & Hesch, C. (2019). A comparison of vibration based bearing fault diagnostic methods. International Journal of Prognostics and Health Management, 10(2).
Qiao, G., & Weiss, B. A. (2016). Advancing measurement science to assess monitoring, diagnostics, and prognostics for manufacturing robotics. International journal of prognostics and health management, 7(Spec Iss on Smart Manufacturing PHM).
Ramasso, E., & Saxena, A. (2014). Performance benchmarking and analysis of prognostic methods for cmapss datasets. International Journal of Prognostics and Health Management, 5(2), 1–15.
Sant’Ana, W. C., Lambert-Torres, G., da Silva, L. E. B., Bonaldi, E. L., de Oliveira, L. E. d. L., Salomon, C. P., & da Silva, J. G. B. (2016). Influence of rotor position on the repeatability of frequency response analysis measurements on rotating machines and a statistical approach for more meaningful diagnostics. Electric Power Systems Research, 133, 71–78.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In 2008 international conference on prognostics and health management (p. 1-17). doi: 10.1109/PHM.2008.4711436
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Shi, J., Peng, D., Peng, Z., Zhang, Z., Goebel, K., & Wu, D. (2022). Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks. Mechanical Systems and Signal Processing, 162, 107996. doi: https://doi.org/10.1016/j.ymssp.2021.107996
Sinha, J. K., & Elbhbah, K. (2013). A future possibility of vibration based condition monitoring of rotating machines. Mechanical Systems and Signal Processing, 34(1-2), 231–240.
Souahli, & Razik. (2020a). Electrical systems 1. from diagnosis to prognosis. Wiley-ISTE.
Souahli, & Razik. (2020b). Electrical systems 2. from diagnosis to prognosis. Wiley-ISTE.
Soualhi, A., Clerc, G., & Razik, H. (2011). Faults classification of induction machine using an improved ant clustering technique. In 8th ieee symposium on diagnostics for electrical machines, power electronics & drives (pp. 316–321).
Soualhi, A., Clerc, G., & Razik, H. (2012). Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Transactions on Industrial Electronics, 60(9), 4053–4062.
Soualhi, A., Medjaher, K., & Zerhouni, N. (2014). Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Transactions on instrumentation and measurement, 64(1), 52–62.
Soualhi, M., Nguyen, K., Medjaher, K., Lebel, D., & Cazaban, D. (2020). Data-driven diagnostics of positioning deviations in multi-axis robots for smart manufacturing. IFAC-PapersOnLine, 53(2), 10330–10335.
Soualhi, M., Nguyen, K. T., & Medjaher, K. (2020). Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 142, 106680.
Soualhi, M., Nguyen, K. T., Medjaher, K., Lebel, D., & Cazaban, D. (2022). Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations. Journal of Intelligent Manufacturing, 1–17.
Soualhi, M., Nguyen, K. T., Soualhi, A., Medjaher, K., & Hemsas, K. E. (2019). Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals. Measurement, 141, 37–51.
Strangas, E., Clerc, G., Razik, H.,&Soualhi, A. (2021). Fault diagnosis, prognosis, and reliability for electrical drives. Wiley-ISTE.
Tamssaouet, F., Nguyen, K. T., Medjaher, K., & Orchard, M. E. (2021). Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects. ISA transactions, 113, 52–63.
Vachtsevanos, G. J., Lewis, F., Roemer, M., Hess, A., & Wu B. (2006). Intelligent fault diagnosis and prognosis for engineering systems (Vol. 456). Wiley Hoboken.
Wang, D., Tsui, K.-L., & Miao, Q. (2017). Prognostics and health management: A review of vibration based bearing and gear health indicators. Ieee Access, 6, 665–676.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151–157.
Zhang, H., Borghesani, P., Randall, R. B., & Peng, Z. (2022). A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings. Mechanical Systems and Signal Processing, 166, 108466.
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A review on deep learning applications in prognostics and health management. Ieee Access, 7, 162415–162438.
Ali, J. B., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015). Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Applied Acoustics, 89, 16–27.
Benaggoune, K., Yue, M., Jemei, S., & Zerhouni, N. (2022). A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell. Applied Energy, 313, 118835. doi: https://doi.org/10.1016/j.apenergy.2022.118835
Brahimi, M., Medjaher, K., Leouatni, M., & Zerhouni, N. (2017). Prognostics and health management for an overhead contact line system-a review. International Journal of Prognostics and Health Management, 8(3).
Breuneval, R., Clerc, G., Nahid-Mobarakeh, B., & Mansouri, B. (2018). Classification with automatic detection of unknown classes based on svm and fuzzy mbf: Application to motor diagnosis. AIMS Electronics and Electrical Engineering, 2(3), 59–84.
Caballé, N. C., Castro, I. T., Pérez, C. J., & Lanza-Gutiérrez, J. M. (2015). A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering & System Safety, 134, 98–109.
Campbell, J. D., Jardine, A. K., & McGlynn, J. (2016). Asset management excellence: optimizing equipment life-cycle decisions. CRC Press.
Casimir, R., Boutleux, E., Clerc, G., & Yahoui, A. (2006). The use of features selection and nearest neighbors rule for faults diagnostic in induction motors. Engineering Applications of Artificial Intelligence, 19(2), 169–177.
Chen, Y., Peng, G., Xie, C., Zhang, W., Li, C., & Liu, S. (2018). Acdin: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis. Neurocomputing, 294, 61–71.
Chiachío, J., Chiachío, M., Sankararaman, S., Saxena, A., & Goebel, K. (2015). Condition-based prediction of time-dependent reliability in composites. Reliability Engineering & System Safety, 142, 134–147.
Cuguero-Escofet, M. A., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1–12.
de Jonge, B., Teunter, R., & Tinga, T. (2017). The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. Reliability engineering & system safety, 158, 21–30.
de Pater, I., & Mitici, M. (2021). Predictive maintenance for multi-component systems of repairables with remaining-useful-life prognostics and a limited stock of spare components. Reliability Engineering & System Safety, 214, 107761.
Goel, A. K., Singh, G., & Naikan, V. N. A. (2022). A methodology for selection of condition monitoring techniques for rotating machinery. International Journal of Prognostics and Health Management, 13(2).
Golafshan, R., & Sanliturk, K. Y. (2016). Svd and hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults. Mechanical Systems and Signal Processing, 70, 36–50.
Gougam, F., Rahmoune, C., Benazzouz, D., & Merainani, B. (2019, sep). Bearing fault diagnosis based on feature extraction of empirical wavelet transform (EWT) and fuzzy logic system (FLS) under variable operating conditions. Journal of Vibroengineering, 21(6), 1636–1650. Retrieved from https://doi.org/10.21595/jve.2019.20092 doi: 10.21595/jve.2019.20092
Gougam, F., Rahmoune, C., Benazzouz, D., Varnier, C., & Nicod, J.-M. (2020). Health monitoring approach of bearing: Application of adaptive neuro fuzzy inference system (anfis) for rul-estimation and autogram analysis for fault-localization. In 2020 prognostics and health management conference (phm-besanc¸on) (p. 200-206). doi: 10.1109/PHM-Besancon49106.2020.00040
Gouriveau, R., Medjaher, K., & Zerhouni, N. (2016). From prognostics and health systems management to predictive maintenance 1: Monitoring and prognostics. John Wiley & Sons.
Jiang, G., Jia, C., Nie, S., Wu, X., He, Q., & Xie, P. (2022). Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals. Measurement, 196, 111159.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328–32338.
Kim, N.-H., An, D., & Choi, J.-H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia cirp, 38, 3–7.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314–334.
Liu, L., Wang, S., Liu, D., Zhang, Y., & Peng, Y. (2015). Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability, 55(9-10), 2092–2096.
Lo, N. G., Soualhi, A., Frin`ı, M., & Razik, H. (2018). Gear and bearings fault detection using motor current signature analysis. In 2018 13th ieee conference on industrial electronics and applications (iciea) (pp. 900–905).
Magnus, K., & Jing. (2019). Vibration-based condition monitoring of heavy duty machine driveline parts: Torque converter, gearbox, axles and bearings. International Journal of Prognostics and Health Management, 10(2).
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012, June). PRONOSTIA : An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management, PHM’12. (Vol. sur CD ROM, pp. 1–8). Denver, Colorado, United States: IEEE Catalog Number: CPF12PHM-CDR.
Nguyen, K. T. P., Medjaher, K., & Tran, D. T. (2022, September). A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines. Artificial Intelligence Review. doi: 10.1007/s10462-022-10260-y
Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., & Zerhouni, N. (2021). Towards an adapted phm approach: Data quality requirements methodology for fault detection applications. Computers in industry, 127, 103414.
Ondel, O., Blanco, E., & Clerc, G. (2007). Beyond the diagnosis: the forecast of state system application in an induction machine. In 2007 ieee international symposium on diagnostics for electric machines, power electronics and drives (pp. 491–496).
Ondel, O., Boutleux, E., & Clerc, G. (2005). Adaptive diagnosis by pattern recognition: Application on an induction machine. In 2005 5th ieee international symposium on diagnostics for electric machines, power electronics and drives (pp. 1–7).
Ooijevaar, T., Pichler, K., Di, Y., & Hesch, C. (2019). A comparison of vibration based bearing fault diagnostic methods. International Journal of Prognostics and Health Management, 10(2).
Qiao, G., & Weiss, B. A. (2016). Advancing measurement science to assess monitoring, diagnostics, and prognostics for manufacturing robotics. International journal of prognostics and health management, 7(Spec Iss on Smart Manufacturing PHM).
Ramasso, E., & Saxena, A. (2014). Performance benchmarking and analysis of prognostic methods for cmapss datasets. International Journal of Prognostics and Health Management, 5(2), 1–15.
Sant’Ana, W. C., Lambert-Torres, G., da Silva, L. E. B., Bonaldi, E. L., de Oliveira, L. E. d. L., Salomon, C. P., & da Silva, J. G. B. (2016). Influence of rotor position on the repeatability of frequency response analysis measurements on rotating machines and a statistical approach for more meaningful diagnostics. Electric Power Systems Research, 133, 71–78.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In 2008 international conference on prognostics and health management (p. 1-17). doi: 10.1109/PHM.2008.4711436
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Shi, J., Peng, D., Peng, Z., Zhang, Z., Goebel, K., & Wu, D. (2022). Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks. Mechanical Systems and Signal Processing, 162, 107996. doi: https://doi.org/10.1016/j.ymssp.2021.107996
Sinha, J. K., & Elbhbah, K. (2013). A future possibility of vibration based condition monitoring of rotating machines. Mechanical Systems and Signal Processing, 34(1-2), 231–240.
Souahli, & Razik. (2020a). Electrical systems 1. from diagnosis to prognosis. Wiley-ISTE.
Souahli, & Razik. (2020b). Electrical systems 2. from diagnosis to prognosis. Wiley-ISTE.
Soualhi, A., Clerc, G., & Razik, H. (2011). Faults classification of induction machine using an improved ant clustering technique. In 8th ieee symposium on diagnostics for electrical machines, power electronics & drives (pp. 316–321).
Soualhi, A., Clerc, G., & Razik, H. (2012). Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Transactions on Industrial Electronics, 60(9), 4053–4062.
Soualhi, A., Medjaher, K., & Zerhouni, N. (2014). Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression. IEEE Transactions on instrumentation and measurement, 64(1), 52–62.
Soualhi, M., Nguyen, K., Medjaher, K., Lebel, D., & Cazaban, D. (2020). Data-driven diagnostics of positioning deviations in multi-axis robots for smart manufacturing. IFAC-PapersOnLine, 53(2), 10330–10335.
Soualhi, M., Nguyen, K. T., & Medjaher, K. (2020). Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mechanical Systems and Signal Processing, 142, 106680.
Soualhi, M., Nguyen, K. T., Medjaher, K., Lebel, D., & Cazaban, D. (2022). Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations. Journal of Intelligent Manufacturing, 1–17.
Soualhi, M., Nguyen, K. T., Soualhi, A., Medjaher, K., & Hemsas, K. E. (2019). Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals. Measurement, 141, 37–51.
Strangas, E., Clerc, G., Razik, H.,&Soualhi, A. (2021). Fault diagnosis, prognosis, and reliability for electrical drives. Wiley-ISTE.
Tamssaouet, F., Nguyen, K. T., Medjaher, K., & Orchard, M. E. (2021). Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects. ISA transactions, 113, 52–63.
Vachtsevanos, G. J., Lewis, F., Roemer, M., Hess, A., & Wu B. (2006). Intelligent fault diagnosis and prognosis for engineering systems (Vol. 456). Wiley Hoboken.
Wang, D., Tsui, K.-L., & Miao, Q. (2017). Prognostics and health management: A review of vibration based bearing and gear health indicators. Ieee Access, 6, 665–676.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151–157.
Zhang, H., Borghesani, P., Randall, R. B., & Peng, Z. (2022). A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings. Mechanical Systems and Signal Processing, 166, 108466.
Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X., & Wei, M. (2019). A review on deep learning applications in prognostics and health management. Ieee Access, 7, 162415–162438.
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