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

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Kang, Weijie, Xiao, Jiyang, Xiao, Mingqing, Hu, Yangguang, Zhu, Haizhen, Li, Jianfeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Confidence intervals; Data models; Evaluation; Feature extraction; Fuzzy Evaluation; Fuzzy logic; Gaussian process; Gaussian Process Regression; Gaussian processes; Gravitational Search Algorithm; Ground penetrating radar; Life assessment; Life prediction; Lithium batteries; Model accuracy; Predictive models; Remaining Life Prediction; Search algorithms; Statistical analysis; Uncertainty; Useful life
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2982223

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.