Health prediction of partially observable failing systems under varying environments

Chaoqun Duan; Yiwei Jiang; Huayan Pu; Jun Luo; Fuqiang Liu; Baoping Tang

doi:10.1016/j.isatra.2023.01.013

Health prediction of partially observable failing systems under varying environments

ISA Trans. 2023 Jun:137:379-392. doi: 10.1016/j.isatra.2023.01.013. Epub 2023 Jan 12.

Authors

Chaoqun Duan¹, Yiwei Jiang², Huayan Pu³, Jun Luo⁴, Fuqiang Liu⁵, Baoping Tang⁶

Affiliations

¹ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China; Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA. Electronic address: chaoqun.duan@hotmail.com.
² School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China. Electronic address: jyw19721691@shu.edu.cn.
³ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China. Electronic address: phygood_2001@shu.edu.cn.
⁴ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China. Electronic address: luojun@shu.edu.cn.
⁵ College of Mechanical Engineering, Chongqing University, Chongqing 400044, China. Electronic address: liufq@cqu.edu.cn.
⁶ College of Mechanical Engineering, Chongqing University, Chongqing 400044, China. Electronic address: bptang@cqu.edu.cn.

PMID: 36740557
DOI: 10.1016/j.isatra.2023.01.013

Abstract

The modern engineering systems often operate under varying environments and only partial information can be observed at discrete monitoring epochs. For such systems, few works have been done for the prognostics of health status using the available environment and monitoring information. Therefore, the aim of this article is to present a new health prediction method for modern engineering systems whose condition is partially observable under varying environments. A dynamic Gamma process is proposed to model the system degradation observations under changing environments. To describe the relation of system actual status to the observed information, a proportional hazard (PH) model integrating internal aging and external observations is presented for modeling the system hazard rate. To realize prediction of residual life of such systems, a matrix operation-based prognostic method is presented to calculate the closed-form solutions of health characteristics for the system. A case study of partially observable failing systems is demonstrated, and comparisons with other recent developed approaches are also given to show the effectiveness of the model.

Keywords: Degradation modeling; Dynamic stochastic process; Prognostics and health management; Residual life prediction.