Prognosis using Bayesian Method by Incorporating Physical Constraints
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Accurately predicting the remaining useful life (RUL) of industrial machinery is crucial for ensuring their reliability and safety. Prognostic methods that rely on Bayesian inference, such as Bayesian method (BM), Kalman and Particle filter (KF, PF), have been extensively studied for the RUL prognosis. However, these algorithms can be affected by noise when training data is limited, and the uncertainty associated with empirical models that are used in place of expensive physics models. As a result, this can lead to significant prediction errors or even infeasible RUL prediction in some cases. To overcome this challenge, three different approaches are proposed to guide the Bayesian framework by incorporating low-fidelity physical information. The proposed approaches embed inequality constraints to reduce sensitivity to local observations and achieve robust prediction. To determine an appropriate approach and its advantageous features, performance is evaluated by both numerical example and real case study for drone motor degradation.
##plugins.themes.bootstrap3.article.details##
Prognostics, Bayesian method, Uncertainty quantification, Remaining useful life
An, D. W., Gang, J. H., Choi, J. H. (2010). MCMC Approach for Parameter Estimation in the Structural Analysis and Prognosis. Journal of the Computational Structural Engineering Institute of Korea, 23(6), 641-649.
Andrieu, C., Jordan, M.I., (2003). An Introduction to MCMC for Machine Learning.
Chen, Y., He, Y., Li, Z., Chen, L., Zhang, C., (2020). Remaining useful life prediction and state of health diagnosis of lithium-ion battery based on second-order central difference particle filter. IEEE Access 8, 37305–37313.
He, W., Williard, N., Osterman, M., Pecht, M., (2011). Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources 196, 10314–10321.
Gebraeel, N., Lei, Y., Li, N., Si, X., Zio, E., (2023). Prognostics and Remaining Useful Life Prediction of Machinery: Advances, Opportunities and Challenges. Journal of Dynamics, Monitoring and Diagnostics.
Kim, N. H., An, D., & Choi, J. H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing. Kim, S., Choi, J.H.,
Kim, N.H., (2022a.) Data-driven prognostics with low-fidelity physical information for digital twin: physics-informed neural network. Structural and Multidisciplinary Optimization 65.
Kim, S., Kim, N.H., Choi, J.H., (2021a). A Study Toward Appropriate Architecture of System-Level Prognostics: Physics-Based and Data-Driven Approaches. IEEE Access 9, 157960–157972.
Kim, S., Park, H.J., Choi, J.H., Kwon, D., (2021b). A Novel Prognostics Approach Using Shifting Kernel Particle Filter of Li-Ion Batteries under State Changes. IEEE Transactions on Industrial Electronics 68, 3485–3493.
Kim, S., Park, H.J., Seo, Y.H., Choi, J.H., (2022b). A Robust Health Indicator for Rotating Machinery under TimeVarying Operating Conditions. IEEE Access 10, 4993– 5001.
Lim, C.K.R., Mba, D., (2015). Switching Kalman filter for failure prognostic. Mech Syst Signal Process 52–53, 426–435.
Son, J., Zhou, S., Sankavaram, C., Du, X., Zhang, Y., (2016). Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter. Reliab Eng Syst Saf 152, 38–50.
Susini, A., n.d. RIMMA Risk Information Management, Risk Models, and Applications.
Sutharssan, T., Stoyanov, S., Bailey, C., Yin, C., (2015). Prognostic and health management for engineering systems: a review of the data‐driven approach and algorithms. The Journal of Engineering, 215–222.
Wang, Q., Xu, K., Kong, X., Huai, T., (2021). A linear mapping method for predicting accurately the RUL of rolling bearing. Measurement (Lond) 176.
Xing, Y., Ma, E.W.M., Tsui, K.L., Pecht, M., (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability 53, 811–820
This work is licensed under a Creative Commons Attribution 3.0 Unported License.