An Adaptive Prognostic Approach for Newly Developed System With Three-Source Variability

Wang, Xi; Hu, Changhua; Si, Xiaosheng; Zheng, Jianfei; Pei, Hong; Yu, Yong; Ren, Ziqiang

doi:10.1109/access.2019.2911307

Cited by 13 publications

(12 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particularly, the PDF curves obtained by the proposed method are much sharper than those obtained by the method in [12], which further demonstrates that considering three-source variability can significantly improve the accuracy of the estimated RUL. Furthermore, the estimated RUL uncertainty of the proposed method decreases continuously as the sampling data are accumulated, which is particularly important in the field of the RUL prognostic and maintenance decision [31], [36]. To further quantify the comparison results, FIG.…”

Section: A Numerical Examplementioning

confidence: 99%

“…By comparing with the results obtained by focusing only on one or two sources variability, it was shown that considering threesource variability simultaneously can greatly improve the accuracy of the model fitting and the performance of the RUL estimation. In addition, the work in [36] developed a RUL prediction method with three-source variability to adaptively update the model parameters and the corresponding RUL distribution via the CM data up to date. However, the results of this work were based on a linear drift model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

Wang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

As the central component of prognostic and health management (PHM) field, remaining useful life (RUL) estimation approaches based on degradation modeling have played an extremely significant role in recent years. For the newly developed systems working in complex environments, the associated degradation processes not only lack historical data and prior information but also have strong nonlinearity and three-source variability. Therefore, this paper proposes an adaptive RUL estimation approach for the newly developed system based on a nonlinear model. Specifically, a general nonlinear Wiener-process-based degradation model is established to simultaneously characterize three-source variability and nonlinearity, and the associated RUL distribution is derived with an explicit form. In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. As such, the RUL distribution can be updated once the new CM data are available. Finally, the effectiveness and superiority of the proposed method are demonstrated by the numerical example an empirical study for battery data. The results show that the proposed method can provide accurate and robust RUL prediction for the newly developed system. INDEX TERMS Remaining useful life, nonlinear, three-source variability, battery, prognostic and health management.

show abstract

Section: A Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

Wang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first way is combining the Bayesian updating and expectation maximization (EM) algorithm, which was first presented by Wang et al [34] for fitting an adapted Brownian motion-based model with a drifting parameter. Then, this updating mechanism has been generalized to the degradation model based on the basic Wiener process [31], [32], nonlinear process [35], [36], degradation process with three sources of variance [37], [38]. Based on the EM algorithm, the RUL estimation results could overcome the influences of imperfect prior information.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Later, Si et al [6,28] applied this algorithm to the basic linear Wiener process. Subsequently, this mechanism was extended to the nonlinear Wiener process [46,47], the linear Wiener process with measurement error [48], the nonlinear Wiener process with measurement error [49,50] and other random effects models [51][52][53][54]. This joint parameter updating method can overcome the influence of imperfect prior information.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

et al. 2020

Self Cite

View full text Add to dashboard Cite

The remaining useful life (RUL) prediction is the core of equipment maintenance and decisionmaking. Accurate RUL prediction can make effective maintenance before the failure occurs to reduce the probability of equipment failure. However, in industrial practice, we often face the situation that prior information is insufficient or inaccurate, which could influence our prediction of RUL and reduce the prediction accuracy severely. To solve this problem, we study the issue of RUL prediction under imperfect prior degradation data by reasonably fusing the prior information and the field degradation data. Firstly, based on the linear Wiener degradation process, we prove a conclusion that the random parameter estimation results by the joint Bayesian algorithm and the expectation-maximization (EM) algorithm are the same as that by the maximum likelihood estimation (MLE) method. It shows that the joint estimation method completely overcomes the influence of prior information, and the more iterations, the smaller proportion of prior information. Secondly, we also prove that the random parameter joint updating method for the nonlinear Wiener degradation process has the same characteristics. Then, a heuristic algorithm that reasonably fuses the prior information and the field degradation data is proposed, which controls the number of iterations of the joint updating algorithm based on the credibility of the prior information. It is also applied to the nonlinear degradation process by not iterating the nonlinear coefficients. Finally, the correctness of the conclusion of this paper is verified by the simulated degradation data, and the effectiveness of the proposed heuristic updating method is verified by the actual lithium battery degradation data.

show abstract

An Adaptive Prognostic Approach for Newly Developed System With Three-Source Variability

Cited by 13 publications

References 38 publications

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

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

Remaining Useful Life Prediction Under Imperfect Prior Degradation Information

Contact Info

Product

Resources

About