Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance

• Autorzy: Li Kangning , Ren Li'na , Li Xueliang , Wang Ziqian

Identyfikatory

DOI

10.17531/ein/174903

• Języki publikacji: EN

Abstrakty

EN

A novel approach for predicting remaining useful life (RUL) is proposed for situations where maintenance threshold and failure threshold exhibit dynamic behavior due to uncertainties in degradation and the influence of detection strategies during maintenance processes. The approach introduces maintenance threshold error to establish a multi-stage maintenance-impact degradation model with dynamic maintenance threshold based on the Wiener process. This model considers the impact of maintenance on degradation rate, amount, and path. Moreover, by using the first hitting time (FHT) and introducing failure threshold error to reflect the dynamic behavior of the failure threshold, the formula for predicting equipment RUL is derived. The model parameters are estimated using both the maximum likelihood estimation (MLE) approach and Bayesian formula. The proposed approach was validated with simulation data and gyroscope degradation data, and the results demonstrate its ability to effectively enhance the precision of equipment RUL prediction.

Słowa kluczowe

EN

dynamic threshold; Wiener process; multi-stage degradation model; remaining useful life

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 1
• Strony: art. no. 174903
• Opis fizyczny: Bibliogr. 36 poz., tab., wykr.

Twórcy

autor

Li Kangning

• Lanzhou University of Technology, China

autor

Ren Li'na

• Lanzhou University of Technology, China

autor

Li Xueliang

• Lanzhou University of Technology, China

autor

Wang Ziqian

• Lanzhou University of Technology, China

Bibliografia

• 1. Changhua H U, Hong P E I, Zhaoqiang W, et al. A new remaining useful life estimation approach for equipment subjected to intervention of imperfect maintenance activities[J]. Chinese Journal of Aeronautics 2018; 31(3): 514-528, https://doi.org/10.1016/j.cja.2018.01.009.
• 2. Du D, Zhang J, Si X, et al. Remaining useful life estimation: A review on stochastic process-based approaches[J]. Recent Patents on Engineering 2021; 15(1): 69-76, https://doi.org/10.2174/1872212114999200423115526.
• 3. Duan F, Wang G, Wei W, et al. Remaining useful life evaluation for accelerated Wiener degradation process model with mixed random effects and measurement errors[J]. Quality and Reliability Engineering International 2023; 39(4): 1334-1351, https://doi.org/10.1002/qre.3296.
• 4. Gao H, Cui L, Dong Q. Reliability modeling for a two-phase degradation system with a change point based on a Wiener process[J]. Reliability Engineering & System Safety 2020; 193: 106601, https://doi.org/10.1016/j.ress.2019.106601.
• 5. Gao H, Cui L, Qiu Q. Reliability modeling for degradation-shock dependence systems with multiple species of shocks[J]. Reliability Engineering & System Safety 2019; 185: 133-143, https://doi.org/10.1016/j.ress.2018.12.011.
• 6. Gao ZY, Li J, Wang RX. Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model. Eksploatacja i Niezawodnosc-Maintenance and Reliability 2021; 23 (1): 154–164, http://dx.doi.org/10.17531/ein.2021.1.16.
• 7. Ge R, Zhai Q, Wang H, et al. Wiener degradation models with scale-mixture normal distributed measurement errors for RUL prediction[J]. Mechanical Systems and Signal Processing 2022; 173: 109029, https://doi.org/10.1016/j.ymssp.2022.109029.
• 8. Gharib H, Kovács G. A Review of Prognostic and Health Management (PHM) Approaches and Limitations for Marine Diesel Engines: New Research Directions[J]. Machines 2023; 11(7): 695,https://doi.org/10.3390/machines11070695.
• 9. Han Y, Ma C, Tang S, et al. Residual life estimation of lithium-ion batteries based on nonlinear Wiener process with measurement error[J]. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 2023; 237(1): 133-151, https://doi.org/10.1177/1748006X221080345.
• 10. Kang J, Lu Y, Zhao B, et al. Remaining useful life prediction of cylinder liner based on nonlinear degradation model[J]. Eksploatacja i Niezawodność-Maintenance and Reliability 2022; 24(1): 62-69, https://doi.org/10.17531/ein.2022.1.8.
• 11. Le Son K, Fouladirad M, Barros A. Remaining useful lifetime estimation and noisy gamma deterioration process[J]. Reliability engineering & system safety 2016; 149: 76-87, https://doi.org/10.1016/j.ress.2015.12.016.
• 12. Lei Y, Li N, Gontarz S, et al. A model-based approach for remaining useful life prediction of machinery[J]. IEEE Transactions on reliability 2016; 65(3): 1314-1326, https://doi.org/10.1109/TR.2016.2570568.
• 13. Li S, Ran Y, Zhang G, et al. Precision Degradation Model and Residual Precision Life Prediction of CNC Machine Tools Based onWiener Process[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing 2021; 1043(3): 032039, https://doi.org/10.1088/1757-899X/1043/3/032039.
• 14. Liao G, Yin H, Chen M, et al. Remaining useful life prediction for multi-phase deteriorating process based on Wiener process[J]. Reliability Engineering & System Safety 2021; 207: 107361, https://doi.org/10.1016/j.ress.2020.107361.
• 15. Lin J, Liao G, Chen M, et al. Two-phase degradation modeling and remaining useful life prediction using nonlinear wiener process[J]. Computers & Industrial Engineering 2021; 160: 107533, https://doi.org/10.1016/j.cie.2021.107533.
• 16. Ling M H, Ng H K T, Tsui K L. Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process[J]. Reliability Engineering & System Safety 2019; 184: 77-85, https://doi.org/10.1016/j.ress.2017.11.017.
• 17. Lyu Y, Jiang Y, Zhang Q, Chen C. Remaining useful life prediction with insufficient degradation data based on deep learning approach.Eksploatacja i Niezawodność -Maintenance and Reliability 2021; 23(4): 745-756, https://doi:10.17531/ein.2021.4.17.
• 18. Ma J, Cai L, Liao G, et al. A multi-phase Wiener process-based degradation model with imperfect maintenance activities[J]. Reliability Engineering & System Safety 2023; 232: 109075, https://doi.org/10.1016/j.ress.2022.109075.
• 19. Pei H, Hu C, Si X, et al. Remaining useful life prediction for nonlinear degraded equipment with bivariate time scales[J]. IEEE Access 2019; 7: 165166-165180, https://doi.org/10.1109/ACCESS.2019.2951804.
• 20. Pei H, Si X, Hu C, et al. A multi-stage Wiener process-based prognostic model for equipment considering the influence of imperfect maintenance activities[J]. Journal of Intelligent & Fuzzy Systems 2018; 34(6): 3695-3705, https://doi.org/10.3233/JIFS-169544.
• 21. Si X S, Zhang Z X, Hu C H. Data-driven remaining useful life prognosis techniques[J]. Beijing, China: National Defense Industry Press and Springer-Verlag GmbH 2017; https://doi.org/10.1007/978-3-662-54030-5.
• 22. Song K, Cui L. A common random effect induced bivariate gamma degradation process with application to remaining useful life prediction[J]. Reliability Engineering & System Safety 2022; 219: 108200, https://doi.org/10.1016/j.ress.2021.108200.
• 23. Sutharssan T, Stoyanov S, Bailey C, et al. Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms[J]. The Journal of Engineering 2015; 2015(7): 215-222. https://doi.org/10.1049/joe.2014.0303.
• 24. Tang S, Yu C, Wang X, et al. Remaining useful life prediction of lithium-ion batteries based on the wiener process with measurement error[J]. energies 2014; 7(2): 520-547, https://doi.org/10.3390/en7020520.
• 25. Wang H, Wang D, Liu H, et al. A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation[J]. Reliability Engineering & System Safety 2022; 225: 108601, https://doi.org/10.1016/j.ress.2022.108601.
• 26. Wang Z Q, Hu C H, Si X S, et al. Remaining useful life prediction of degrading systems subjected to imperfect maintenance: Application to draught fans[J]. Mechanical Systems and Signal Processing 2018; 100: 802-813, https://doi.org/10.1016/j.ymssp.2017.08.016.
• 27. Wang Z Q, Hu C H, Wang W B, et al. A simulation-based remaining useful life prediction approach considering the influence of maintenance activities[C]// 2014 IEEE Conference on Prognostics and Health Management (PHM) 2014; 284-289, https://doi.org/10.1109/PHM.2014.6988180.
• 28. Wang Z, Ta Y, Cai W, et al. Research on a remaining useful life prediction approach for degradation angle identification two-stage degradation process[J]. Mechanical Systems and Signal Processing 2023; 184: 109747, https://doi.org/10.1016/j.ymssp.2022.109747.
• 29. Wen Y, Wu J, Das D, et al. Degradation modeling and RUL prediction using Wiener process subject to multiple change points andunit heterogeneity[J]. Reliability Engineering & System Safety 2018; 176: 113-124, https://doi.org/10.1016/j.ress.2018.04.005.
• 30. Wu D, Jia M, Cao Y, et al. Remaining useful life estimation based on a nonlinear Wiener process model with CSN random effects[J]. Measurement 2022; 205: 112232, https://doi.org/10.1016/j.measurement.2022.112232
• 31. Xu X, Yu C, Tang S, et al. Remaining useful life prediction of lithium-ion batteries based on Wiener processes with considering the relaxation effect[J]. Energies 2019; 12(9): 1685, https://doi.org/10.3390/en12091685.
• 32. Xu XD, Tang SJ, Yu CQ, Xie J, Han XB, Ouyang MG. Remaining useful life prediction of lithium-ion batteries based on Wiener process under time-varying temperature condition. Reliability Engineering and System Safety 2021; 214: 107675, https://doi.org/10.1016/j. ress.2021.107675.
• 33. Ye Z S, Chen N, Shen Y. A new class of Wiener process models for degradation analysis[J]. Reliability Engineering & System Safety 2015; 139: 58-67, https://doi.org/10.1016/j.ress.2015.02.005.
• 34. Yu WN, Tu WB, Kim Y, Mechefske C. A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability. Reliability Engineering and System Safety 2021; 212: 107631, https://doi.org/10.1016/j.ress.2021.107631.
• 35. Zhai QQ, Ye ZS. RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Transactions on Industrial Informatics 2017; 13(6); 2911-2921, https://doi.org/10.1109/TII.2017.2684821
• 36. Zhang H, Chen M, Shang J, et al. Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion[J]. Science China Information Sciences 2021; 64(7): 171201, https://doi.org /10.1007/S11432-020-3134-8.