Degradation Modeling Based on a Time-Dependent Ornstein-Uhlenbeck Process and Residual Useful Lifetime Estimation

Deng, Yingjun; Barros, Anne; Grall, Antoine

doi:10.1109/tr.2015.2462353

Cited by 39 publications

(17 citation statements)

References 33 publications

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“…A time-dependent Ornstein-Uhlenbeck (OU) Process is widely used to describe physical dynamics of systems which stabilize at its equilibrium point. In the field of reliability modeling, OU process could be a good candidate for modeling the degradation process when the drift is time-dependent and also depends on the degradation state [31,33] . Indeed for commonly used processes such as drifted Brownian motion [30] , the mean or the drift trend can be chosen quite freely, but the variance strongly depends on the stochastic process properties.…”

Section: : Ornstein-uhlenbeck Processmentioning

confidence: 99%

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Model selection for degradation modeling and prognosis with health monitoring data

Nguyen

Fouladirad

Grall

2018

Reliability Engineering & System Safety

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Section: : Ornstein-uhlenbeck Processmentioning

confidence: 99%

“…The remaining useful lifetime pdf of this OU process can be numerically evaluated as the solution of a non-singular Volterra integral equation, see [33] .…”

Section: : Ornstein-uhlenbeck Processmentioning

confidence: 99%

Model selection for degradation modeling and prognosis with health monitoring data

Nguyen

Fouladirad

Grall

2018

Reliability Engineering & System Safety

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“…However, the OU process is still in the exploratory stage in degradation modelling without a widely used form. 15,16 Since the degradation process is an irreversible process for most products such that they can only accumulate damage but not restore themselves, 17 the monotonic stochastic processes, such as the gamma process and IG process, are a natural choice for degradation analysis. Compared with the gamma process and Wiener process, the IG process is more flexible for incorporating random effects and explanatory variables, and is mathematically tractable, especially for certain degradation data.…”

Section: Introductionmentioning

confidence: 99%

Inverse Gaussian–based model with measurement errors for degradation analysis

Chen

Xin-li

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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To build more credible degradation models, it is necessary to consider measurement errors in degradation analysis. This article proposes an inverse Gaussian-based state space model with measurement errors that can capture the unit-to-unit variability of the degradation rate by incorporating a random effect. Then, the lifetime distribution and alarm probabilities are derived. Under the non-Gaussian assumptions, conventional parameter estimation algorithms cannot be applied directly. Therefore, an improved expectation–maximization algorithm that is combined with particle methods is developed to estimate parameters. Finally, this article concludes with a simulation study and two case applications to demonstrate the applicability and advantages of the proposed model.

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“…Fang [16] and Bao [17], respectively, proposed a stochastic model updating, and solved it based on the Monte Carlo method. Deng [18] established a time-dependent degradation model, but did not consider the randomness of variables at each moment. Zhang [19] described the random effects of degradation modeling and remaining useful life, and the time-dependent functions were used to fit the degradation rules of characteristic parameters of random variables.…”

Section: Introductionmentioning

confidence: 99%

A Time-Variant Reliability Model for Copper Bending Pipe under Seawater-Active Corrosion Based on the Stochastic Degradation Process

Sun

Liao

et al. 2018

Materials

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In the degradation process, the randomness and multiplicity of variables are difficult to describe by mathematical models. However, they are common in engineering and cannot be neglected, so it is necessary to study this issue in depth. In this paper, the copper bending pipe in seawater piping systems is taken as the analysis object, and the time-variant reliability is calculated by solving the interference of limit strength and maximum stress. We did degradation experiments and tensile experiments on copper material, and obtained the limit strength at each time. In addition, degradation experiments on copper bending pipe were done and the thickness at each time has been obtained, then the response of maximum stress was calculated by simulation. Further, with the help of one kind of Monte Carlo method we propose, the time-variant reliability of copper bending pipe was calculated based on the stochastic degradation process and interference theory. Compared with traditional methods and verified by maintenance records, the results show that the time-variant reliability model based on the stochastic degradation process proposed in this paper has better applicability in the reliability analysis, and it can be more convenient and accurate to predict the replacement cycle of copper bending pipe under seawater-active corrosion.

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Degradation Modeling Based on a Time-Dependent Ornstein-Uhlenbeck Process and Residual Useful Lifetime Estimation

Cited by 39 publications

References 33 publications

Model selection for degradation modeling and prognosis with health monitoring data

Model selection for degradation modeling and prognosis with health monitoring data

Inverse Gaussian–based model with measurement errors for degradation analysis

A Time-Variant Reliability Model for Copper Bending Pipe under Seawater-Active Corrosion Based on the Stochastic Degradation Process

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