Estimation of machinery's remaining useful life in the presence of non-Gaussian noise by using a robust extended Kalman filter

被引:3
|
作者
Shiri, Hamid [1 ]
Zimroz, Pawel [1 ]
Wylomanska, Agnieszka [2 ]
Zimroz, Radoslaw [1 ]
机构
[1] Wroclaw Univ Sci & Technol, Fac Geoengn Min & Geol, Grobli 15, PL-50421 Wroclaw, Poland
[2] Wroclaw Univ Sci & Technol, Fac Pure & Appl Math, Hugo Steinhaus Ctr, Wyspianskiego 27, PL-50370 Wroclaw, Poland
关键词
Prognostics; Remaining useful life (RUL); Non-linear degradation; Extended Kalman filter; Robust methods; Non-Gaussian noise; PREDICTION; CORRENTROPY; PROGNOSTICS; DEGRADATION; NONSTATIONARY; DIAGNOSIS;
D O I
10.1016/j.measurement.2024.114882
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Estimation of the remaining useful life (RUL) of industrial machinery is essential for condition -based maintenance (CBM). While numerous papers have explored this issues, challenges arise as machinery often works in non -stationary conditions, particularly in harsh environments (like mining machines, wind turbines, helicopters, etc.). The data collected from such environments are affected by non -Gaussian noise, posing difficulties for traditional approaches to non-linear state estimation or prediction. The widely used extended Kalman filter (EKF) suffers from the non -Gaussian noise effect due to its recursive minimum L2 -norm filtering. To address these issues, we propose a robust EKF based on the maximum correntropy criterion. This method effectively estimates the RUL of the time -varying degradation process in the presence of non -Gaussian noise, also enabling confidence interval computation for uncertainty management. The efficiency of our approach was confirmed through application to simulated and benchmark data sets, outperforming Kalman filter -based methods for both simulated and real -world scenarios.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Nanosatellite attitude estimation using Kalman-type filters with non-Gaussian noise
    Cilden-Guler, Demet
    Raitoharju, Matti
    Piche, Robert
    Hajiyev, Chingiz
    AEROSPACE SCIENCE AND TECHNOLOGY, 2019, 92 : 66 - 76
  • [32] Remaining Useful Life Prediction of Lithium Batteries Based on Extended Kalman Particle Filter
    Zhang, Ning
    Xu, Aidong
    Wang, Kai
    Han, Xiaojia
    Hong, Wenhuan
    Hong, Seung Ho
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2021, 16 (02) : 206 - 214
  • [33] Robust direction-of-arrival estimation in non-Gaussian noise
    Yardimci, Y
    Cetin, AE
    Cadzow, JA
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1998, 46 (05) : 1443 - 1451
  • [34] Robust direction-of-arrival estimation in non-Gaussian noise
    Bilkent Univ, Ankara, Turkey
    IEEE Trans Signal Process, 5 (1443-1451):
  • [35] Non-Gaussian parameter estimation using generalized polynomial chaos expansion with extended Kalman filtering
    Sen, Subhamoy
    Bhattacharya, Baidurya
    STRUCTURAL SAFETY, 2018, 70 : 104 - 114
  • [36] Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise
    Izanloo, Reza
    Fakoorian, Seyed Abolfazl
    Yazdi, Hadi Sadoghi
    Simon, Dan
    2016 ANNUAL CONFERENCE ON INFORMATION SCIENCE AND SYSTEMS (CISS), 2016,
  • [37] Robust Signal-to-Noise Ratio Estimation in Non-Gaussian Noise Channel
    Lo, Ying-Siew
    Lim, Heng-Siong
    Tan, Alan Wee-Chiat
    WIRELESS PERSONAL COMMUNICATIONS, 2016, 91 (02) : 561 - 575
  • [38] Robust Signal-to-Noise Ratio Estimation in Non-Gaussian Noise Channel
    Ying-Siew Lo
    Heng-Siong Lim
    Alan Wee-Chiat Tan
    Wireless Personal Communications, 2016, 91 : 561 - 575
  • [39] A novel fusion maximum correntropy Kalman/UFIR filter for state estimation with uncertain non-Gaussian noise statistics
    Liu, Zheng
    Zhang, Min
    Song, Xinmin
    Yan, Xuehua
    MEASUREMENT, 2023, 220
  • [40] Bayesian Unscented Kalman Filter for State Estimation of Nonlinear and Non-Gaussian Systems
    Liu, Zhong
    Chan, Shing-Chow
    Wu, Ho-Chun
    Wu, Jiafei
    2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2016, : 443 - 447