Predictive maintenance (PdM) is an effective approach to enhancing system availability and reducing maintenance costs. However, existing PdM strategies often focus on maintenance decisions for individual components, with limited optimization of system-level maintenance strategies. To address this gap, this paper proposes a multi-component system PdM strategy based on uncertain process and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-least square (LS). First, considering the impact of epistemic uncertainty, a Remaining Useful Life (RUL) prediction method based on an uncertain process is proposed. CEEMDAN is employed to denoise the available measurements, followed by the estimation of the unknown parameters of the uncertain degradation model using the least squares method. Second, uncertain simulation is combined with kernel density estimation (KDE) to obtain the probabilistic RUL of the equipment. Subsequently, a preventive replacement strategy is designed by integrating the RUL PDF and the configuration of multi-component systems. Finally, the proposed approach is validated using the NASA lithium-ion battery degradation dataset. Experimental results demonstrate that the proposed RUL prediction method achieves higher prediction accuracy compared to traditional uncertain process, and nonlinear Wiener methods. Moreover, the proposed multi-component system maintenance strategy effectively prevents system failures.
ZioE.Prognostics and health management (PHM): where are we and where do we (need to) go in theory and practice. Reliab Eng Syst Saf2022; 218: 108119.
2.
ChenBLiPWanG, et al. A method for predicting the remaining useful life of aircraft engines based on NLSTM and feature optimization strategy. IEEE Internet Things J2024; 11: 36410–36419.
3.
WangLZhuZZhaoX.Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method. Reliab Eng Syst Saf2024; 245: 110012.
4.
ChenCShiJShenM, et al. A predictive maintenance strategy using deep learning quantile regression and kernel density estimation for failure prediction. IEEE Trans Instrum Meas2023; 72: 1–12.
5.
XiaTJiangYDingY, et al. Intelligent maintenance framework for reconfigurable manufacturing with deep-learning-based prognostics. IEEE Internet Things J2024; 11: 22853–22868.
6.
ZhuangLXuAWangXL.A prognostic driven predictive maintenance framework based on bayesian deep learning. Reliab Eng Syst Saf2023; 234: 109181.
7.
DingGChenH.A rul prediction method for lithium-ion batteries based on improved singular spectrum analysis and CSA-KELM. Microelectron Reliab2023; 144: 114975.
8.
de PaterIMiticiM. Predictive maintenance for multi-component systems of repairables with remaining-useful-life prognostics and a limited stock of spare components. Reliab Eng Syst Saf2021; 214: 107761.
9.
WangLChenYZhaoX, et al. Predictive maintenance scheduling for aircraft engines based on remaining useful life prediction. IEEE Internet Things J2024; 11: 23020–23031.
10.
XuZZhangYMiaoQ.An attention-based multi-scale temporal convolutional network for remaining useful life prediction. Reliab Eng Syst Saf2024; 250: 110288.
11.
DowneyALuiYHHuC, et al. Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliab Eng Syst Saf2019; 182: 1–12.
12.
CuiLLiWLiuD, et al. A novel robust dual unscented particle filter method for remaining useful life prediction of rolling bearings. IEEE Trans Instrum Meas2024; 73: 1–9.
13.
ZhouJQinYLuoJ, et al. Remaining useful life prediction by distribution contact ratio health indicator and consolidated memory gru. IEEE Trans Ind Inform2023; 19(7): 8472–8483.
14.
XuZZhangYMiaoJ, et al. Global attention mechanism based deep learning for remaining useful life prediction of aero-engine. Measurement2023; 217: 113098.
15.
LyuGZhangHZhangY, et al. An interpretable remaining useful life prediction scheme of lithium-ion battery considering capacity regeneration. Microelectron Reliab2022; 138: 114625.
16.
Forouzandeh ShahrakiAAl-DahidiSRahim TaleqaniA, et al. Using LSTM neural network to predict remaining useful life of electrolytic capacitors in dynamic operating conditions. Proc Inst Mech Eng O J Risk Reliab2023; 237(1): 16–28.
17.
CuiJCaoLZhangT.A two-stage gaussian process regression model for remaining useful prediction of bearings. Proc Inst Mech Eng O J Risk Reliab2024; 238(2): 333–348.
18.
LiJWangKHouX, et al. A dual-scale transformer-based remaining useful life prediction model in industrial internet of things. IEEE Internet Things J2024; 11: 26656–26667.
19.
ZhouJQiJChenD, et al. Continuous remaining useful life prediction by self-guided attention convolutional neural network and memory consciousness adjustment. IEEE Internet Things J2024; 11: 31947–31958.
20.
HuangCGYinXHuangHZ, et al. An enhanced deep learning-based fusion prognostic method for rul prediction. IEEE Trans Reliab2020; 69(3): 1097–1109.
21.
LiYChenYHuZ, et al. Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models. Reliab Eng Syst Saf2023; 229: 108869.
22.
JinRChenZWuK, et al. Bi-lstm-based two-stream network for machine remaining useful life prediction. IEEE Trans Instrum Meas2022; 71: 1–10.
23.
ZhangSJKangRLinYH.Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process. Reliab Eng Syst Saf2021; 208: 107440.
24.
WangYLiuQLuW, et al. A general time-varying wiener process for degradation modeling and rul estimation under three-source variability. Reliab Eng Syst Saf2023; 232: 109041.
25.
GuoJWangZLiH, et al. A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear wiener process. Reliab Eng Syst Saf2024; 245: 110014.
26.
ZhangSZhaiQShiX, et al. A wiener process model with dynamic covariate for degradation modeling and remaining useful life prediction. IEEE Trans Reliab2023; 72(1): 214–223.
27.
LiQLinJNiuP.Remaining useful life prediction method for jointless track circuits based on multivariate feature fusion and nonlinear wiener process. Meas Sci Technol2025; 36(1): 016118.
28.
WuJPKangRLiXY.Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension. Reliab Eng Syst Saf2020; 201: 106967.
29.
KimSKimNHChoiJH.Prediction of remaining useful life by data augmentation technique based on dynamic time warping. Mech Syst Signal Process2020; 136: 106486.
30.
LiuB.Uncertainty theory. Springer, 2010.
31.
de PaterIReijnsAMiticiM. Alarm-based predictive maintenance scheduling for aircraft engines with imperfect remaining useful life prognostics. Reliab Eng Syst Saf2022; 221: 108341.
32.
DongEZhangYZhanX, et al. A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear wiener process. Meas Sci Technol2024; 35(12): 126210.
33.
ChenCTaoGShiJ, et al. A lithium-ion battery degradation prediction model with uncertainty quantification for its predictive maintenance. IEEE Trans Ind Electron2024; 71(4): 3650–3659.
34.
WangZShangguanWPengC, et al. A predictive maintenance strategy for a single device based on remaining useful life prediction information: a case study on railway gyroscope. IEEE Trans Instrum Meas2024; 73: 1–14.
35.
WangQBuSHeZ.Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Trans Ind Inform2020; 16(10): 6509–6517.
36.
CaoXPengK.Multi-phase degradation modeling and remaining useful life prediction considering aleatory and epistemic uncertainty. IEEE Sens J2023; 23: 27757–27770.
37.
ShengYYaoKChenX.Least squares estimation in uncertain differential equations. IEEE Trans Fuzzy Syst2020; 28(10): 2651–2655.
38.
CaiYLiWZahidT, et al. Early prediction of remaining useful life for lithium-ion batteries based on CEEMDAN-transformer-DNN hybrid model. Heliyon2023; 9(7): e17754.
39.
TorresMEColominasMASchlotthauerG, et al. A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2011, pp.4144–4147. IEEE.
40.
SilvermanBW.Density estimation for statistics and data analysis. Routledge, 2018.
41.
SahaandBGoebelK.Battery data set, NASA Ames prognostics data repository. NASA Ames Research Center, 2007.
42.
CatelaniMCianiLFantacciR, et al. Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network. IEEE Trans Instrum Meas2021; 70: 1–11.
43.
SiXSWangWHuCH, et al. Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res2011; 213(1): 1–14.
44.
SiXSWangWHuCH, et al. A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech Syst Signal Process2013; 35(1–2): 219–237.
45.
HanWZhangL.Battery cell reconfiguration to expedite charge equalization in series-connected battery systems. IEEE Robot Autom Lett2018; 3(1): 22–28.
46.
ChiangSJLiawCMChangWC, et al. Multi-module parallel small battery energy storage system. IEEE Trans Energy Convers1996; 11(1): 146–154.
47.
XuDTianYShiJ, et al. Reliability analysis and optimal redundancy for a satellite power supply system based on a new dynamic k-out-of-n: G model. Reliab Eng Syst Saf2023; 236: 109317.
