Remaining useful life prediction based on the mixed effects model with mixture prior distribution

Kontar, Raed Al; Son, Junbo; Zhou, Shiyu; Sankavaram, Chaitanya; Zhang, Yilu; Du, Xinyu

doi:10.1080/24725854.2016.1263771

Cited by 28 publications

(13 citation statements)

References 25 publications

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“…Although the unit-to-unit variation could be captured by random effects, the model is still focus on population level based on failure-time model. As a results, the RUL prediction is on population level, not on individual level [15].…”

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confidence: 89%

“…Gebraeel et al [23] proposed a Bayesian approach for parameter updating of mixed effect models, and online sensor data was employed for RUL prediction of a specific unit. Kontar et al [15] extended this approach with mixture prior distributions considering the different grouping of historical data. Hamada [22] analyzed the case that reciprocal of the slope in the linear model satisfies the Weibull distribution and uses the Bayesian method to estimate the parameters of the model.…”

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confidence: 99%

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Train Wheel Degradation Modeling and Remaining Useful Life Prediction Based on Mixed Effect Model Considering Dependent Measurement Errors

Zhou

Jiang

et al. 2019

IEEE Access

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In order to predict the remaining useful life of rail train wheels progressively under dependent measurement errors, an extended mixed effect degradation model and Bayesian parameter update framework is proposed. The heteroscedastic and correlated structure of error terms are considered in the modeling process, and the spectral decomposition of the error effect matrix is used to realize the independent conversion of online monitoring data. Based on the Bayesian method, the online monitoring data is merged with historical knowledge for parameter updating, and remaining useful life prediction and reliability evaluation are performed. The case study results show that the proposed method considering dependent measurement errors could provide adequate degradation modeling and improve the accuracy of remaining useful life prediction. These results will help the development of prognostic and health management of train wheel. INDEX TERMS Condition monitoring, Bayesian methods, remaining life assessment, mixed effect models, degradation modeling, spectral decomposition. I. INTRODUCTION

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confidence: 89%

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confidence: 99%

Train Wheel Degradation Modeling and Remaining Useful Life Prediction Based on Mixed Effect Model Considering Dependent Measurement Errors

Zhou

Jiang

et al. 2019

IEEE Access

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“…There has been extensive literature on the extrapolation of longitudinal signals under a single stream setting. However, literature has mainly focused on parametric models due to their computational efficiency and ease of implementation (Gebraeel et al, 2005;Gebraeel & Pan, 2008;Si et al, 2012;Kontar et al, 2017). Such models have been applied in healthcare, manufacturing and mobility applications specifically to understand the remaining useful life of operational units.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Bayesian mixed effect model with a general polynomial function whose degree is determined through an Akaike information criteria (AIC) (Rizopoulos, 2011;Son et al, 2013;Kontar et al, 2017). We denote this methods as ME.…”

Section: General Settingsmentioning

confidence: 99%

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data

Chung¹,

Kontar²

2019

Preprint

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The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution of multi-stream longitudinal data for an in-service unit through borrowing strength from other historical units. Our approach first decomposes each stream into a linear combination of eigenfunctions and their corresponding functional principal component (FPC) scores. A Gaussian process prior for the FPC scores is then established based on a functional semi-metric that measures similarities between streams of historical units and the in-service unit. Finally, an empirical Bayesian updating strategy is derived to update the established prior using real-time stream data obtained from the in-service unit. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the-art approaches and can effectively account for heterogeneity as well as achieve high predictive accuracy.

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“…Data-driven approaches have been conducted in terms of battery fading dynamics prediction of various fields due to easy implementation, inexpensive cost, and less complexity. Statistical methods, as one kind of datadriven approach, are mainly based on the collected time-tofailure data in which prior knowledge is needed such as short voltage sequence [7,8].…”

Section: Introductionmentioning

confidence: 99%

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

et al. 2020

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We report an integrated methodology (FMC-XGBoost) that mainly consists of a five-state nonhomogeneous Markov chain model (FMC) and XGBoost model. Unlike those existing methods in which capacity fading processes are assumed to be irreversible, the proposed integrated methodology can combine user-specific driving patterns (UDP) and capacity recovery effects (CRE) to predict battery fading dynamics even with partially available data for an individual battery. The parameters of the constructed FMC model are linked to the known physicochemical and material properties of Li-ion battery fading dynamics, which aims to cognize and predict the primary fading dynamics, and the proposed XGBoost model is to cognizes and predicts the fluctuation dynamics regarding UDP & CRE. To comprehensively verify the capabilities of the proposed integrated methodology, a series of cases and comparisons are conducted and analysed based on partial available fading data by selecting batteries to simulate situations of individual differences and different UDPs & CREs. The averages of MAE, MRE and RMSE are approximately 0.0128, 0.9251%, and 0.0153 respectively even when only 60% of the data are available. All verifications and comparison analyses reveal that the proposed integrated methodology provides an accurate, robust, stable, and general way to cognize and predict battery fading dynamics during usage, and subsequently to alleviate range anxiety for batteries in real applications. INDEX TERMS FMC-XGBoost, fading dynamics prediction, user-specific driving patterns, capacity recovery effects, Li-ion battery, range anxiety.

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Remaining useful life prediction based on the mixed effects model with mixture prior distribution

Cited by 28 publications

References 25 publications

Train Wheel Degradation Modeling and Remaining Useful Life Prediction Based on Mixed Effect Model Considering Dependent Measurement Errors

Train Wheel Degradation Modeling and Remaining Useful Life Prediction Based on Mixed Effect Model Considering Dependent Measurement Errors

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data

Degradation Dynamics Cognition and Prediction of Li-Ion Battery: An Integrated Methodology for Alleviating Range Anxiety

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