Lifetime prognostics for deteriorating systems with time-varying random jumps

Zhang, Jianxun; Hu, Changhua; He, Xiao; Si, Xiaosheng; Liu, Yang; Zhou, Donghua

doi:10.1016/j.ress.2017.05.047

Cited by 30 publications

(17 citation statements)

References 33 publications

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“…For a more accurate approximation of the posterior density, a "burn-in" stage is conducted to exclude samples from the beginning of the MCMC, which are far away from the stationary distribution [36]. It should be noted that for systems not influenced by hard failures, like those in [27] and [28], generally no catastrophic…”

Section: Updating Shock Intensity Using Mh Samplingmentioning

confidence: 99%

“…The distribution of RUL k can, then, be derived by substituting (28) and (26) into (25). In this paper, due to the complexities in (26) and (28), we develop a simulation-based method for RUL prediction, as shown in Algorithm 3.…”

Section: Inputs: θmentioning

confidence: 99%

“…Ke et al [27] investigated RUL prediction for a nonstationary degradation process with random shocks; a modified Kalman filter model was developed to estimate the system hidden degradation state and the degradation model parameters were estimated by maximum likelihood estimation with the expectation maximization algorithm; to apply the model, the shock arrival time must be known. Zhang et al [28] considered a system subject to degradation and time-varying random jumps, and developed an approximated analytical solution for the predicted lifetime, where a two-step expectation conditional maximization algorithm was used for estimating model parameters.…”

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confidence: 99%

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A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes

et al. 2019

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A sequential Bayesian approach is presented for remaining useful life (RUL) prediction of dependent competing failure processes (DCFP). The DCFP considered comprises of soft failure processes due to degradation and hard failure processes due to random shocks, where dependency arises due to the abrupt changes to the degradation processes brought by the random shocks. In practice, random shock processes are often unobservable, which makes it difficult to accurately estimate the shock intensities and predict the RUL. In the proposed method, the problem is solved recursively in a two-stage framework: in the first stage, parameters related to the degradation processes are updated using particle filtering, based on the degradation data observed through condition monitoring; in the second stage, the intensities of the random shock processes are updated using the Metropolis-Hastings algorithm, considering the dependency between the degradation and shock processes, and the fact that no hard failure has occurred. The updated parameters are, then, used to predict the RUL of the system. Two numerical examples are considered for demonstration purposes and a real dataset from milling machines is used for application purposes. Results show that the proposed method can be used to accurately predict the RUL in DCFP conditions.

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Section: Updating Shock Intensity Using Mh Samplingmentioning

confidence: 99%

Section: Inputs: θmentioning

confidence: 99%

mentioning

confidence: 99%

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A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes

et al. 2019

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“…Some experiments show that the degradation process consists of gradual and sudden changes (Son et al, 2014;Kundu et al, 2019). The RUL prediction has to consider the degradation process of equipment in response to the uncertain influence of sudden changes, such as Zhang et al (2017). They proposed a jump diffusion process for describing the corresponding damages.…”

Section: Introductionmentioning

confidence: 99%

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

Yang

Zheng

2020

Computers in Industry

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“…As such, it is meaningful to investigate how to estimate the lifetime and remaining useful life of Lithium-Ion battery. As an essential part of prognostic and health management (PHM), lifetime or remaining useful life (RUL) estimation can provide an effective information for maintenance decision to avoid the accident caused by its failure and reduce the safety risk [5][6][7]. So far, the methods of the lifetime estimation have been widely researched and gained momentum [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Zhang

et al. 2017

Energies

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Abstract:A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE) is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM) algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

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Lifetime prognostics for deteriorating systems with time-varying random jumps

Cited by 30 publications

References 33 publications

A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes

A Sequential Bayesian Approach for Remaining Useful Life Prediction of Dependent Competing Failure Processes

A method for degradation prediction based on Hidden semi-Markov models with mixture of Kernels

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

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