A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation

Si, Xiaosheng; Wang, Wenbin; Hu, Chang Hua; Chen, Mao Yin; Zhou, Dong

doi:10.1016/j.ymssp.2012.08.016

Cited by 385 publications

(247 citation statements)

References 47 publications

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“…The Wiener process can provide a good description of system's dynamic characteristic due to its non-monotonic property, infinite divisibility property and physical interpretations. As such, it has been widely used to model degradation process, such as bridge beams [26], milling machines [27], light-emitting diodes (LEDs) [28,29], micro electro mechanical systems (MEMS) [30], laser generators [31], continuous stirred tank reactors [32], and gyros [33][34][35]. The RUL prediction based on the Wiener process, which is a type of statistics-based data driven methods, has gained much attention in recent years [36].…”

Section: Introductionmentioning

confidence: 99%

“…The work about this Bayesian updating mechanism for the WPME is also been studied in [41]. Another updating algorithm is combining the Bayesian updating and expectation maximization (EM) algorithm, where the Bayesian updating includes the basic Bayesian algorithm, Kalman filtering, strong tracking filter, extended Kalman filter, etc., [13,32,34,35,47]. In this combination algorithm, the Bayesian updating is used to update the random parameters of the model and the EM algorithm is used to estimate the fixed parameters.…”

Section: Introductionmentioning

confidence: 99%

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Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

et al. 2014

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Abstract:Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

et al. 2014

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“…And our approach can obtain the regular PDF of RUL which could be convenient for maintenance decision. It is shown in Figure 11 that the traditional approaches [8,9] could not obtain regular PDF of RUL.…”

Section: Case 2: the Fluctuations Of Degradation Datamentioning

confidence: 99%

“…Under a recognized definition of lifetime, that is, first hitting time (FHT) [6,7], many RUL estimation approaches have been reported in literature [8][9][10]. References [6,11] provide a detailed overview of the degradation modeling and RUL predicting method.…”

Section: Introductionmentioning

confidence: 99%

Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

Zhang

Zhou

et al. 2018

Journal of Control Science and Engineering

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Remaining useful life (RUL) prediction method based on degradation trajectory has been one of the most important parts in prognostics and health management (PHM). In the conventional model, the degradation data are usually used for degradation modeling directly. In engineering practice, the degradation of many systems presents a volatile situation, that is, fluctuation. In fact, the volatility of degradation data shows the stability of system, so it could be used to reflect the performance of system. As such, this paper proposes a new degradation model for RUL estimation based on the volatility of degradation data. Firstly the degradation data are decomposed into trend items and random items, which are defined as a stochastic process. Then the standard deviation of the stochastic process is defined as another performance variable because standard deviation reflects the system performance. Finally the Wiener process and the normal stochastic process are used to model the trend items and random items separately, and then the probability density function (PDF) of the RUL is obtained via a redefined failure threshold function that combines the trend items and the standard deviation of the random items. Two practical case studies demonstrate that, compared with traditional approaches, the proposed model can deal with the degradation data with many fluctuations better and can get a more reasonable result which is convenient for maintenance decision.

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“…The Wiener process is particularly a good candidate to represent the evolution of a degradation process that is made of an increasing trend over time with random Gaussian noise, both being proportional to elapsed time. It is characterized by continuous sample paths and independent, (non-)stationary and normally distributed increments [16].…”

Section: The Wiener Processmentioning

confidence: 99%

Updated Operational Reliability from Degradation Indicators and Adaptive Maintenance Strategy

Letot¹,

Equeter²,

Dutoit³

et al. 2017

System Reliability

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This chapter is dedicated to the reliability and maintenance of assets that are characterized by a degradation process. The item state is related to a degradation mechanism that represents the unit-to-unit variability and time-varying dynamics of systems. The maintenance scheduling has to be updated considering the degradation history of each item. The research method relies on the updating process of the reliability of a specific asset. Given a degradation process and costs for preventive/corrective maintenance actions, an optimal inspection time is obtained. At this time, the degradation level is measured and a prediction of the degradation is conducted to obtain the next inspection time. A decision criterion is established to decide whether the maintenance action should take place at the current time or postpone. Consequently, there is an optimal number of inspections that allows to extend the useful life of an asset before performing the preventive maintenance action. A numerical case study involving a non-stationary Wiener-based degradation process is proposed as an illustration of the methodology. The results showed that the expected cost per unit of time considering the adaptive maintenance strategy is lower than the expected cost per unit of time obtained for other maintenance policies.

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A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation

Cited by 385 publications

References 47 publications

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Estimating Remaining Useful Life for Degrading Systems with Large Fluctuations

Updated Operational Reliability from Degradation Indicators and Adaptive Maintenance Strategy

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