Research on Remaining Useful Life Prognostics Based on Fuzzy Evaluation-Gaussian Process Regression Method

Abstract

To achieve efficient and accurate remaining life prediction and effectively express the uncertainty of prediction results, this paper proposes a remaining life prediction method based on fuzzy evaluation-Gaussian process regression (FE-GPR). First, the prediction of the remaining useful life (RUL) is affected by unknown variables, such as the environment, and it is difficult to achieve accurate predictions. It is necessary to effectively express the uncertainty of such prediction results. In this paper, we have put forward a RUL prediction method based on GPR, which can realize the RUL prediction with a confidence interval. Second, combined with the characteristics of the GPR method, an observation data preprocessing method based on fuzzy evaluation is proposed. The initial fuzzy evaluation method is established based on expert knowledge. Then, the classification nodes are optimized by the gravitational search algorithm (GSA) and historical data. This method, which uses fuzzy logic combined with expert knowledge, can avoid over-fitting in the case of limited data, and effectively improves the prediction accuracy of the GPR model. Finally, we use NASA PCoE. lithium battery data for a case study. The results show that the FE-GPR method achieves a more accurate RUL prediction and effectively reflects the uncertainty of the prediction results.

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

“…A BPNN [28] is a multilayer feedforward neural network in an artificial neural network, which is the most representative and extensive approach. The GPR [29], [34]- [36] is designed based on the Bayesian framework [37], [38] and is widely used in prediction tasks [39]- [41]. In SOH estimation, the inputs for the BPNNbased method, the GPR-based method, and the standard SVM-based method are also HF3 and HF4.…”

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

“…For the long-term RUL prediction of the battery, the RMSE and MAE are no more than 10 cycles and 6 cycles, respectively. Kang et al (2020) proposed an RUL prediction method based on Fuzzy Evaluation Gaussian Process Regression (FE-GPR). Combined with the characteristics of the GPR method, the observation data is preprocessed through fuzzy evaluation.…”

Technology development and commercial applications of industrial fault diagnosis system: a review

“…Researchers usually combine fuzzy logic with other techniques for getting better prognosis performance. Kang et al [148] applied fuzzy evaluation-Gaussian process regression model to estimate the RUL in the case of limited data. Cheng et al [149] developed a novel RUL prediction method using adaptive neuro-fuzzy inference system (AFNIS) and particle filtering to automatically detect gearboxes faults in wind turbines.…”

“…12 The RUL prediction results with different prediction starting point and prediction methods [169] the short-term state of health of lithium-ion batteries and applied GPR model to assess the RUL by the mapping relationship between state of health and RUL. Kang et al [148] used Gaussian process regression based on fuzzy evaluation to achieve RUL estimation of the lithium battery. They found that the proposed approach can avoid over-fitting in the case of finite data.…”