A Novel Lifetime Estimation Method for Two-Phase Degrading Systems

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

doi:10.1109/tr.2018.2829844

Cited by 74 publications

(74 citation statements)

References 46 publications

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“…Following Gebraeel et al [11], some related issues and many variants and applications have been studied and reported [12]- [14]. Also, this strategy under Bayesian framework has been applied in Wiener processes based degradation model, which is a very popular model for modeling the degradation modeling and has been widely used to model the degradation process in systems [15], such as lithium-ion batteries [16], [17], LCDs (Liquid crystal display) [18], gyroscopes [19], bearings [20], etc. Si et al [21] did a lot of interesting works in this research area for the degradation process with three sources of variance, e.g.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

et al. 2020

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Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method. INDEX TERMS Remaining useful life prediction, wiener processes, fusing, failure time data, field degradation data, random effects, Bayesian framework.

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Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

et al. 2020

Self Cite

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“…Because of the conciseness and feasibility, the condition monitoring (CM) data–based methods are quite popular in RUL prediction. Specifically, without any demand of expert knowledge or extra experiments, data‐driven methods have occupied a significant place in the existing literature, such as the utilization of Brownian motion (BM, also refers to the Wiener process) and fractional Brownian motion (FBM) . These stochastic models possess remarkable similarities that their described degradation processes should follow the specified distribution forms, and hence would facilitate the identification of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the BM family–based methods concentrate on complicated diffusion properties, rather than Gamma process and inverse Gaussian process, which can only deal with monotone data. Zhang et al exploited a two‐phase Wiener process to model the uncertainty of degradation processes with changing points. Wang et al made an adaptive improvement of the Wiener process under the assumption that the degradation rate and variation satisfy a proportional relationship.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Zhou

Chen

et al. 2020

Quality & Reliability Eng

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Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

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“…Wang et al, [17] and Yan et al, [16] proposed a reliability evaluation method for LCD based on the two-phase Wiener process. Recently, Wang et al, [23,24] utilized the Bayesian rule and change-point Wiener process to model the degradation process of PDPs and LED, and Zhang et al, [25] attained some analytical results of lifetime’s distribution based on the two-phase Wiener process.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Estimation for Multi-Phase Deteriorating Process with Random Abrupt Jumps

Zhang

et al. 2019

Sensors

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Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime’s closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on Expectation Maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration.

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A Novel Lifetime Estimation Method for Two-Phase Degrading Systems

Cited by 74 publications

References 46 publications

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Lifetime Estimation for Multi-Phase Deteriorating Process with Random Abrupt Jumps

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