Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIM.2017.2717278
Title: Robust Degradation Analysis With Non-Gaussian Measurement Errors
Authors: ZHAI QINGQING 
YE ZHISHENG 
Issue Date: 21-Jul-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: ZHAI QINGQING, YE ZHISHENG (2017-07-21). Robust Degradation Analysis With Non-Gaussian Measurement Errors. IEEE Transactions on Instrumentation and Measurement 66 (11) : 2803-2812. ScholarBank@NUS Repository. https://doi.org/10.1109/TIM.2017.2717278
Abstract: Degradation analysis is an effective way to infer the health status and lifetime of products. Due to variability in the measurement, degradation observations are often subject to measurement errors. Existing studies generally assume Gaussian measurement errors, which may be deficient when there are outliers in the observations. To make a robust inference, we propose a Wiener degradation model with measurement errors modeled by Student's t-distribution. The t-distribution is a useful extension to the Gaussian distribution that provides a parametric approach to robust statistics. Nevertheless, the resulting likelihood function involves multiple integrals, which makes direct maximization difficult. Therefore, we propose an expectation-maximization algorithm, where the variational Bayes technique is introduced to derive an approximate conditional distribution in the E-step. The effectiveness of the proposed model is validated through Monte Carlo simulations. The applicability of the robust method is illustrated through applications to the degradation data of lithium-ion batteries and hard disk drives.
Source Title: IEEE Transactions on Instrumentation and Measurement
URI: https://scholarbank.nus.edu.sg/handle/10635/168379
ISSN: 00189456
15579662
DOI: 10.1109/TIM.2017.2717278
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