Application of particle filter variants to estimate the remaining useful life

被引：0
|
作者：
Banerjee, Ahin [1 ]
Bajpai, Mayank [1 ]
Putcha, Chandrasekhar [2 ]
机构：
[1] Indian Inst Technol BHU, Dept Civil Engn, Varanasi 221005, India
[2] Calif State Univ Fullerton, Dept Civil & Environm Engn, Fullerton, CA USA
来源：
PROBABILISTIC ENGINEERING MECHANICS | 2023年 / 74卷
关键词：
Remaining useful life; Particle filter; Resampling; Motor; Degradation data; LITHIUM-ION BATTERY; RESAMPLING METHODS; STATE ESTIMATION; KALMAN FILTER; PROGNOSTICS; PREDICTION; TUTORIAL;
D O I：
10.1016/j.probengmech.2023.103531
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Dynamic loadings encountered by clutch motor of an Automated manual transmission (AMT) passenger vehicle require apposite assessment of nonlinear characteristic of the degrading system to accurately predict the remaining useful life (RUL) for implementation of condition based maintenance (CBM) schedule. The present study involves the application of three variants of particle filter (PF) with various resampling techniques to account for heavy tailed observations and non-Gaussian characteristic of noise to improve the accuracy of RUL estimation. Degradation dataset was generated using accelerated life cycle test (ALCT) on hardware-in-loop (HIL) laboratory of a large Indian automotive company. A comparative analysis of the results showed 91.5, 93.2, and 94.9% enhancement in the efficacy of RUL estimation due to canonical, unscented, and improved unscented particle filters (i-UPF) vis-a-vis the ordinarily fitted exponential degradation model.
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页数：12
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