Online robust degradation analysis with measurement outlier

Publication Type

Journal Article

Publication Date

2-2025

Abstract

Online degradation analysis requires an adaptive model parameter estimation. In addition, measurement errors and outliers are inevitable in real applications of degradation analysis. However, existing online models ignore the measurement error or assume the measurement error to be distributed as Gaussian for mathematical simplicity, which is vulnerable to measurement outliers. To deal with such problems, an online degradation analysis technique with robustness to measurement outliers is developed. More specifically, the underlying degradation is modeled with the Wiener process and the measurement error is modeled by constructing a modified Huber density to enhance the robustness against the outlier. For the adaptive estimation of model parameters, an online expectation-maximization (EM) algorithm is developed. Furthermore, procedures are provided for recursive degradation state identification by maximizing a posteriori based on the Laplace approximation. Numerical and two real case studies are carried out to validate the efficacy of the proposed model.

Keywords

Laplace approximation, maximizing a posteriori, modified Huber density, online expectation-maximization (EM), Wiener process

Discipline

Management Sciences and Quantitative Methods | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Integrative Research Areas

Publication

IEEE Transactions on Instrumentation and Measurement

Volume

74

First Page

1

Last Page

12

ISSN

0018-9456

Identifier

10.1109/TIM.2025.3541658

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIM.2025.3541658

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