RUL Prognostics Recursive Bayesian Ensemble Prediction with Combining Artificial Degradation Patterns
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Published
Nov 10, 2023
Junhyun Byun
Suhong Min
Jihoon Kang
Abstract
With the rising complexity of manufacturing processes, resulting from rapid industrial development, the utilization of remaining useful lifecycle (RUL) prediction, based on failure physics and traditional reliability, has remained limited. Although data-driven approaches of RUL prediction were developed using machine learning algorithms, uncertainty-induced challenges have emerged, such as sensor noise and modeling error. To address these uncertainty-induced problems, this study proposes a stochastic ensemble-modeling concept for improving the RUL prediction result. The proposed ensemble model combines artificial degradation patterns and fitness weights, which incorporate formulas reflecting failure patterns and various reliability function data with the observed degradation factor. Furthermore, a recursive Bayesian updating technique, reflecting the difference between expected and observed remaining life sequentially, was leveraged to reduce the prediction uncertainty. Moreover, we comparatively studied the predictive performance of the proposed model (recursive Bayesian ensemble model) against an existing baseline method (exponentially weighted linear regression model). Through simulation and case datasets, this experiment demonstrated the robustness and utility of the proposed algorithm.
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Keywords
Artificial degradation pattern, Bayesian update, fitness weight, predictive maintenance, prognostic, reliability information, RUL
References
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Berger, V. W., & Zhou, Y. (2014). Kolmogorov–smirnov test: Overview. Wiley statsref: Statistics reference online.
Bienefeld, C., Kirchner, E., Vogt, A., & Kacmar, M. (2022). On the importance of temporal information for remaining useful life prediction of rolling bearings using a random forest regressor. Lubricants, 10(4), 67.
Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
Catelani, M., Ciani, L., Grasso, F., Patrizi, G., & Reatti, A. (2022, July). Remaining Useful Life estimation for electric vehicle batteries using a similarity-based approach. In 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) (pp. 82- 87). IEEE.
Chen, Z., Cao, S., & Mao, Z. (2017). Remaining useful life estimation of aircraft engines using a modified similarity and supporting vector machine (SVM) approach. Energies, 11(1), 28.
Chang, S. Y., & Wu, H. C. (2021). Tensor recursive least squares filters for multichannel interrelational signals. IEEE Transactions on Signal and Information Processing over Networks, 7, 562-577.
Cheng, S., & Pecht, M. (2007). Multivariate state estimation technique for remaining useful life prediction of electronic products. Parameters, 1, x2.
Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems, 91, 179-189.
Hashemian, H. M. (2010). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and measurement, 60(1), 226-236.
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.
Hjorth, U. (1980). A reliability distribution with increasing, decreasing, constant and bathtub-shaped failure rates. Technometrics, 22(1), 99-107.
Hong, S., Qin, C., Lai, X., Meng, Z., & Dai, H. (2023). Stateof-health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm. Journal of Energy Storage, 64, 107179.
Keizers, L., Loendersloot, R., & Tinga, T. (2021). Unscented kalman filtering for prognostics under varying operational and environmental conditions. International Journal of Prognostics and Health Management, 12(2).
Lee, J., Qiu, H., Yu, G., & Lin, J. (2007). Bearing data set, nasa ames prognostics data repository. Rexnord Technical Services, IMS, University of Cincinnati. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic -data-repository/
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.
Lin, Y. H., Ding, Z. Q., & Li, Y. F. (2023). Similarity based remaining useful life prediction based on Gaussian Process with active learning. Reliability Engineering & System Safety, 109461.
Liu, D., Luo, Y., Peng, Y., Peng, X., & Pecht, M. (2012). Lithium-ion battery remaining useful life estimation based on nonlinear AR model combined with degradation feature. In Annual Conference of the PHM Society (Vol. 4, No. 1).
Long, T., Wang, S., Cao, W., Zhou, H., & Fernandez, C. (2023). An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries. Electrochimica acta, 450, 142270.
Ma, M., Sun, C., Mao, Z., & Chen, X. (2021). Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery. ISA transactions, 113, 166-174.
Ma, Z., Zhang, W., He, J., & Jin, H. (2022, May). MultiParameter Online Identification of Permanent Magnet Synchronous Motor Based on Dynamic Forgetting Factor Recursive Least Squares. In 2022 IEEE 5th International Electrical and Energy Conference (CIEEC) (pp. 4865-4870). IEEE.
Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78.
Nannapaneni, S., Dubey, A., Abdelwahed, S., Mahadevan, S., Neema, S., & Bapty, T. (2016). Mission-based reliability prediction in component-based systems. International Journal of Prognostics and Health Management, 7(1).
Pecht, M., & Gu, J. (2009). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3-4), 309-322.
Peng, Y., Hou, Y., Song, Y., Pang, J., & Liu, D. (2018). Lithium-ion battery prognostics with hybrid Gaussian process function regression. Energies, 11(6), 1420.
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of sound and vibration, 289(4-5), 1066-1090.
Radaideh, M. I., Pappas, C., Wezensky, M., Ramuhalli, P., & Cousineau, S. (2023). Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning. International Journal of Prognostics and Health Management, 14(1).
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation–a review on the statistical data driven approaches. European journal of operational research, 213(1), 1-14.
Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014, August). Log-based predictive maintenance. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1867-1876).
Star, M., & McKee, K. (2021). Remaining Useful Life Estimation Using Neural Ordinary Differential Equations. International Journal of Prognostics and Health Management, 12(2).
Tian, H., Yang, L., & Ju, B. (2023). Spatial correlation and temporal attention-based LSTM for remaining useful life prediction of turbofan engine. Measurement, 214, 112816.
Wang, G., Li, H., Zhang, F., & Wu, Z. (2022). Feature fusion based ensemble method for remaining useful life prediction of machinery. Applied Soft Computing, 129, 109604.
Zhang, Y., Liu, L., Peng, Y., & Liu, D. (2018). An electromechanical actuator motor voltage estimation method with a feature-aided Kalman filter. Sensors, 18(12), 4190.
Zhou, J., Qin, Y., Chen, D., Liu, F., & Qian, Q. (2022). Remaining useful life prediction of bearings by a new reinforced memory GRU network. Advanced Engineering Informatics, 53, 101682.
Zhu, Y., Xu, B., Luo, Z., Liu, Z., Wang, H., & Du, C. (2022, September). Prediction method of turbine engine RUL based on GA-SVR. In 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) (pp. 1-6). IEEE.
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners-Review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8(3), 1- 31.
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2012, June). Fault prognostic of bearings by using support vector data description. In 2012 IEEE Conference on Prognostics and Health Management (pp. 1-7). IEEE.
Berger, V. W., & Zhou, Y. (2014). Kolmogorov–smirnov test: Overview. Wiley statsref: Statistics reference online.
Bienefeld, C., Kirchner, E., Vogt, A., & Kacmar, M. (2022). On the importance of temporal information for remaining useful life prediction of rolling bearings using a random forest regressor. Lubricants, 10(4), 67.
Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
Catelani, M., Ciani, L., Grasso, F., Patrizi, G., & Reatti, A. (2022, July). Remaining Useful Life estimation for electric vehicle batteries using a similarity-based approach. In 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) (pp. 82- 87). IEEE.
Chen, Z., Cao, S., & Mao, Z. (2017). Remaining useful life estimation of aircraft engines using a modified similarity and supporting vector machine (SVM) approach. Energies, 11(1), 28.
Chang, S. Y., & Wu, H. C. (2021). Tensor recursive least squares filters for multichannel interrelational signals. IEEE Transactions on Signal and Information Processing over Networks, 7, 562-577.
Cheng, S., & Pecht, M. (2007). Multivariate state estimation technique for remaining useful life prediction of electronic products. Parameters, 1, x2.
Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems, 91, 179-189.
Hashemian, H. M. (2010). State-of-the-art predictive maintenance techniques. IEEE Transactions on Instrumentation and measurement, 60(1), 226-236.
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.
Hjorth, U. (1980). A reliability distribution with increasing, decreasing, constant and bathtub-shaped failure rates. Technometrics, 22(1), 99-107.
Hong, S., Qin, C., Lai, X., Meng, Z., & Dai, H. (2023). Stateof-health estimation and remaining useful life prediction for lithium-ion batteries based on an improved particle filter algorithm. Journal of Energy Storage, 64, 107179.
Keizers, L., Loendersloot, R., & Tinga, T. (2021). Unscented kalman filtering for prognostics under varying operational and environmental conditions. International Journal of Prognostics and Health Management, 12(2).
Lee, J., Qiu, H., Yu, G., & Lin, J. (2007). Bearing data set, nasa ames prognostics data repository. Rexnord Technical Services, IMS, University of Cincinnati. https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic -data-repository/
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.
Lin, Y. H., Ding, Z. Q., & Li, Y. F. (2023). Similarity based remaining useful life prediction based on Gaussian Process with active learning. Reliability Engineering & System Safety, 109461.
Liu, D., Luo, Y., Peng, Y., Peng, X., & Pecht, M. (2012). Lithium-ion battery remaining useful life estimation based on nonlinear AR model combined with degradation feature. In Annual Conference of the PHM Society (Vol. 4, No. 1).
Long, T., Wang, S., Cao, W., Zhou, H., & Fernandez, C. (2023). An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries. Electrochimica acta, 450, 142270.
Ma, M., Sun, C., Mao, Z., & Chen, X. (2021). Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery. ISA transactions, 113, 166-174.
Ma, Z., Zhang, W., He, J., & Jin, H. (2022, May). MultiParameter Online Identification of Permanent Magnet Synchronous Motor Based on Dynamic Forgetting Factor Recursive Least Squares. In 2022 IEEE 5th International Electrical and Energy Conference (CIEEC) (pp. 4865-4870). IEEE.
Massey Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. Journal of the American statistical Association, 46(253), 68-78.
Nannapaneni, S., Dubey, A., Abdelwahed, S., Mahadevan, S., Neema, S., & Bapty, T. (2016). Mission-based reliability prediction in component-based systems. International Journal of Prognostics and Health Management, 7(1).
Pecht, M., & Gu, J. (2009). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3-4), 309-322.
Peng, Y., Hou, Y., Song, Y., Pang, J., & Liu, D. (2018). Lithium-ion battery prognostics with hybrid Gaussian process function regression. Energies, 11(6), 1420.
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of sound and vibration, 289(4-5), 1066-1090.
Radaideh, M. I., Pappas, C., Wezensky, M., Ramuhalli, P., & Cousineau, S. (2023). Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning. International Journal of Prognostics and Health Management, 14(1).
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation–a review on the statistical data driven approaches. European journal of operational research, 213(1), 1-14.
Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014, August). Log-based predictive maintenance. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1867-1876).
Star, M., & McKee, K. (2021). Remaining Useful Life Estimation Using Neural Ordinary Differential Equations. International Journal of Prognostics and Health Management, 12(2).
Tian, H., Yang, L., & Ju, B. (2023). Spatial correlation and temporal attention-based LSTM for remaining useful life prediction of turbofan engine. Measurement, 214, 112816.
Wang, G., Li, H., Zhang, F., & Wu, Z. (2022). Feature fusion based ensemble method for remaining useful life prediction of machinery. Applied Soft Computing, 129, 109604.
Zhang, Y., Liu, L., Peng, Y., & Liu, D. (2018). An electromechanical actuator motor voltage estimation method with a feature-aided Kalman filter. Sensors, 18(12), 4190.
Zhou, J., Qin, Y., Chen, D., Liu, F., & Qian, Q. (2022). Remaining useful life prediction of bearings by a new reinforced memory GRU network. Advanced Engineering Informatics, 53, 101682.
Zhu, Y., Xu, B., Luo, Z., Liu, Z., Wang, H., & Du, C. (2022, September). Prediction method of turbine engine RUL based on GA-SVR. In 2022 International Conference on Artificial Intelligence and Computer Information Technology (AICIT) (pp. 1-6). IEEE.
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Technical Papers