A Model for Residual Life Prediction Based on Brownian Motion in Framework of Similarity

Zhang, Huihui; Hu, Changhua; Kong, Xiangyu; Zhang, Wei

doi:10.1002/asjc.1209

Cited by 5 publications

(9 citation statements)

References 27 publications

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“…To integrate the population degradation information and individual degradation data, Hao and Su [51] proposed a Bayesian framework for RUL prediction on model (1). Utilizing historical degradations with failure and suspension events, Zhang et al [52] developed the framework of a similarity-based model, in which the RUL was obtained by comparing the similarity between the operating and reference devices. An empirical two-stage estimation method was developed when the parameters in the multiphase degradation are assumed to be random [53].…”

Section: Linear Drift Modelsmentioning

confidence: 99%

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

Zhang

Chen

Shang

et al. 2021

Sci. China Inf. Sci.

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Brownian motion (BM) has been widely used for degradation modeling and remaining useful life (RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence (LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion (FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

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Section: Linear Drift Modelsmentioning

confidence: 99%

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

Zhang

Chen

Shang

et al. 2021

Sci. China Inf. Sci.

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“…As in [14] and [15], the similarity between the operating device's HI and a reference is measured over a time interval I by a similarity function S. For the similarity computation, (N + 1) consecutive monitoring points are considered. In particularly, the last (N + 1) observations of the operating device's HI are chosen as they reflect the current state of the device.…”

Section: Similaritymentioning

confidence: 99%

“…In [13], it is used with an adaptive drift parameter to model the HI of the operating device and to predict its future state for RUL prediction, based on its past degradation information and R2F indicator. In [14], the BM is introduced in the similaritybased approach. This category of approach employs all the R2F profiles generated in the same operating conditions of the operating device to predict its RUL [15].…”

Section: Introductionmentioning

confidence: 99%

“…These R2F profiles similar to the operating device's HI provide additional contributions to the prediction. Thus, in our work we combine the strengths of these two methods by using the adaptive BM model proposed in [13] that introduces the references's information and the operating device's HI after a similarity computation as in [14]. However, the problem is that the R2F data are sometimes limited as the devices are not allowed to be used until failure for finance and safety issues.…”

Section: Introductionmentioning

confidence: 99%

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Combining Approaches of Brownian Motion and Similarity Principle to Improve the Remaining Useful Life Prediction

Jamal

Al-Kharaz

Ananou

et al. 2021

2021 IEEE International Conference on Prognostics and Health Management (ICPHM)

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This paper proposes a data-driven framework for Remaining Useful Life (RUL) prediction, based on the Brownian Motion model (BM) and the similarity principle, for an operating system given its health indicator. It addresses the issues of noisy and limited run-to-failure (R2F) data. The Percentile filtering is used to extract, from the R2F data, 100 monotonic profiles used as references in the modeling and the RUL prediction. Then, the similarity is computed between these references and the Health Indicator (HI) of the operating system. Fitting the most similar reference into the BM improves the RUL prediction. A numerical application using simulated data justifies the accuracy of this approach.

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“…of these data-driven approaches, challenges still exist for their practical applications [4], [22]: 1) to build accurate models, data-driven approaches require sufficiently representative run-to-failure data (i.e., time series data up to the threshold value beyond which the equipment loses its functionality) which, in some practical cases, might be expensive or impractical to obtain; for this reason, data-driven approaches are more commonly applied for equipment of relatively short life than for safety-critical and slow-degrading equipment, for which complete run-to-failure trajectories are rarely available [4], [28];…”

Section: Notation and List Of Acronymsmentioning

confidence: 99%

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

Al‐Dahidi

Maio

Baraldi

et al. 2016

Reliability Engineering & System Safety

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Abstract. The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminum electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature. Keywords

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A Model for Residual Life Prediction Based on Brownian Motion in Framework of Similarity

Cited by 5 publications

References 27 publications

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

Combining Approaches of Brownian Motion and Similarity Principle to Improve the Remaining Useful Life Prediction

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

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