Open Access

ARTICLE

Robust Remaining Useful Life Estimation Based on an Improved Unscented Kalman Filtering Method

Shenkun Zhao, Chao Jiang^*, Zhe Zhang, Xiangyun Long

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China

* Corresponding Author: Chao Jiang. Email:

(This article belongs to the Special Issue: Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures)

Computer Modeling in Engineering & Sciences 2020, 123(3), 1151-1173. https://doi.org/10.32604/cmes.2020.08867

Received 19 October 2019; Accepted 11 February 2020; Issue published 28 May 2020

Abstract

In the Prognostics and Health Management (PHM), remaining useful life (RUL) is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems. Recently, unscented Kalman filtering (UKF) has been applied widely in the RUL estimation. For a degradation system, the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF. However, in some special degradation systems, their monitored measurements have a linear relation with their degradation states. For these special problems, it may bring estimation errors to use the UKF method directly. Besides, many uncertain factors can result in the fluctuations of the estimated results, which may have a bad influence on the RUL estimation method. As a result, a robust RUL estimation approach is proposed in this paper to reduce the errors and randomness of estimation results for this kind of degradation problems. Firstly, an improved unscented Kalman filtering is established utilizing the Kalman filtering (KF) method and a linear adaptive strategy. The linear adaptive strategy is used to adjust its noise term adaptively. Then, the robust RUL estimation is realized by the improved UKF. At last, three problems are investigated to demonstrate the effectiveness of the proposed method.

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Keywords

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