SCIENCE CHINA Information Sciences, Volume 62, Issue 2: 022202(2019) https://doi.org/10.1007/s11432-017-9347-5

Remaining useful life prediction for multi-component systems with hidden dependencies

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  • ReceivedSep 25, 2017
  • AcceptedJan 31, 2018
  • PublishedDec 27, 2018


How can we predict the remaining useful life (RUL) of a dynamic system subject to multiple dependent degradations? Is it possible to address the above problem when the degradation information is obtained indirectly? According to a new type of state space-based model, we mainly develop an online RUL prediction method for the above system. In this model, the dependencies among different degradations can be reflected in a diffusion coefficient matrix. Considering that some industrial systems like blast furnaces are usually equipped with multi-sensors, an efficient information fusion strategy also plays an important role in predicting the RUL. Based on multi-dimensional observations, the hidden degradation states are identified through the sequential Kalman filtering. Meanwhile, the unknown parameters in the model are updated iteratively by the expectation maximization (EM) algorithm. At last, the RUL distributions are simulated through the Monte Carlo method, in which three types of failure structures with regard to the degradations are considered. The effectiveness of the proposed method is fully verified by a numerical example as well as a case study about the blast furnace.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61490701, 61290324, 61473164) and Research Fund for the Taishan Scholar Project of Shandong Province of China.


[1] Sikorska J Z, Hodkiewicz M, Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Processing, 2011, 25: 1803-1836 CrossRef ADS Google Scholar

[2] Okoh C, Roy R, Mehnen J. Overview of Remaining Useful Life Prediction Techniques in Through-life Engineering Services. Procedia CIRP, 2014, 16: 158-163 CrossRef Google Scholar

[3] Xi X, Chen M, Zhou D. Remaining Useful Life Prediction for Degradation Processes With Memory Effects. IEEE Trans Rel, 2017, 66: 751-760 CrossRef Google Scholar

[4] Goebel K F. Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques. Dissertation for Ph.D. Degree. Berkeley: University of California, 1996. Google Scholar

[5] Ahmadzadeh F, Lundberg J. Remaining useful life estimation: review. Int J Syst Assur Eng Manag, 2014, 5: 461-474 CrossRef Google Scholar

[6] Si X, Wang W, Hu C, et al. Remaining useful life estimation-a review on the statistical data driven approaches. Eur J Oper Res, 2011, 213: 1--14. Google Scholar

[7] Wei M, Chen M, Zhou D. Multi-Sensor Information Based Remaining Useful Life Prediction With Anticipated Performance. IEEE Trans Rel, 2013, 62: 183-198 CrossRef Google Scholar

[8] Niu G, Yang B S. Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Syst Appl, 2010, 37: 8831-8840 CrossRef Google Scholar

[9] Li R, Ryan J K. A Bayesian Inventory Model Using Real-Time Condition Monitoring Information. Production Operations Manage, 2011, 20: 754-771 CrossRef Google Scholar

[10] Tang S, Yu C, Wang X. Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error. Energies, 2014, 7: 520-547 CrossRef Google Scholar

[11] Bian L, Gebraeel N. Stochastic framework for partially degradation systems with continuous component degradation-rate-interactions. Naval Res Logistics, 2014, 61: 286-303 CrossRef Google Scholar

[12] Wang X, Guo B, Cheng Z. Residual life estimation based on bivariate Wiener degradation process with measurement errors. J Cent South Univ, 2012, 20: 1844--1851. Google Scholar

[13] Xi Z, Jing R, Wang P. A copula-based sampling method for data-driven prognostics. Reliability Eng Syst Saf, 2014, 132: 72-82 CrossRef Google Scholar

[14] Shi H, Zeng J. Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Comput Industrial Eng, 2016, 93: 192-204 CrossRef Google Scholar

[15] Khorasgani H, Biswas G, Sankararaman S. Methodologies for system-level remaining useful life prediction. Reliability Eng Syst Saf, 2016, 154: 8-18 CrossRef Google Scholar

[16] Rodrigues L R. Remaining Useful Life Prediction for Multiple-Component Systems Based on a System-Level Performance Indicator. IEEE/ASME Trans Mechatron, 2018, 23: 141-150 CrossRef Google Scholar

[17] Prakash O, Samantaray A K, Bhattacharyya R. Model-based multi-component adaptive prognosis for hybrid dynamical systems. Control Eng Practice, 2018, 72: 1-18 CrossRef Google Scholar

[18] Mercier S, Pham H H. A preventive maintenance policy for a continuously monitored system with correlated wear indicators. Eur J Operational Res, 2012, 222: 263-272 CrossRef Google Scholar

[19] Wang X, Guo B, Cheng Z. Residual life estimation based on bivariate Wiener degradation process with time-scale transformations. J Statistical Computation Simul, 2014, 84: 545-563 CrossRef Google Scholar

[20] Wei M H. Multi-sensor monitoring information based remaining useful life prediction for industrial equipments. Dissertation for Ph.D. Degree. Beijing: Tsinghua University, 2013. Google Scholar

[21] Liao H, Elsayed E A. Reliability inference for field conditions from accelerated degradation testing. Naval Res Logistics, 2006, 53: 576-587 CrossRef Google Scholar

[22] Trevisanello L, Meneghini M, Mura G. Accelerated Life Test of High Brightness Light Emitting Diodes. IEEE Trans Device Mater Relib, 2008, 8: 304-311 CrossRef Google Scholar

[23] Ye Z S, Wang Y, Tsui K L. Degradation Data Analysis Using Wiener Processes With Measurement Errors. IEEE Trans Rel, 2013, 62: 772-780 CrossRef Google Scholar

[24] Wang X, Balakrishnan N, Guo B. Residual life estimation based on a generalized Wiener degradation process. Reliability Eng Syst Saf, 2014, 124: 13-23 CrossRef Google Scholar

[25] Ye Z S, Chen N, Shen Y. A new class of Wiener process models for degradation analysis. Reliability Eng Syst Saf, 2015, 139: 58-67 CrossRef Google Scholar

[26] Xi X P, Chen M Y, Zhou D H. Online prognostics based on multiple dependent degradation processes. In: Proceedings of Prognostics and System Health Management Conference (PHM), Harbin, 2017. 1--6. Google Scholar

[27] Kao Y H, Van Roy B. Directed Principal Component Analysis. Operations Res, 2014, 62: 957-972 CrossRef Google Scholar

[28] Tipping M E, Bishop C M. Probabilistic Principal Component Analysis. J R Statistical Soc B, 1999, 61: 611-622 CrossRef Google Scholar

[29] Chui C K, Chen G. Kalman Filtering. Berlin: Springer, 2009. Google Scholar

[30] Shumway R H, Stoffer D S. Time Series Analysis and Its Applications. New York: Springer, 2000. Google Scholar

[31] Houtekamer P L, Mitchell H L. Ensemble Kalman filtering. Q J R Meteorol Soc, 2005, 131: 3269-3289 CrossRef ADS Google Scholar

[32] Sun S L. Multi-sensor optimal fusion fixed-interval Kalman smoothers. Inf Fusion, 2008, 9: 293-299 CrossRef Google Scholar

[33] Lemieux C. Monte Carlo and Quasi-Monte Carlo Sampling. New York: Springer, 2009. Google Scholar

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