Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter

Junbo Son, Shiyu Zhou, Chaitanya Sankavaram, Xinyu Du and Yilu Zhang

Reliability Engineering and System Safety, 2016, vol. 152, issue C, 38-50

Abstract: In this paper, a statistical prognostic method to predict the remaining useful life (RUL) of individual units based on noisy condition monitoring signals is proposed. The prediction accuracy of existing data-driven prognostic methods depends on the capability of accurately modeling the evolution of condition monitoring (CM) signals. Therefore, it is inevitable that the RUL prediction accuracy depends on the amount of random noise in CM signals. When signals are contaminated by a large amount of random noise, RUL prediction even becomes infeasible in some cases. To mitigate this issue, a robust RUL prediction method based on constrained Kalman filter is proposed. The proposed method models the CM signals subject to a set of inequality constraints so that satisfactory prediction accuracy can be achieved regardless of the noise level of signal evolution. The advantageous features of the proposed RUL prediction method is demonstrated by both numerical study and case study with real world data from automotive lead-acid batteries.

Keywords: Remaining useful life; Condition monitoring signals; Constrained Kalman filter (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:152:y:2016:i:c:p:38-50

DOI: 10.1016/j.ress.2016.02.006

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