A weight-allocation-based ensemble remaining useful life prediction approach for a single device

Abstract

Accurate prediction of the remaining useful life (RUL) can improve device reliability and reduce maintenance costs. In real working conditions, the absence of historical data for devices of the same type and the unlabeled degradation data for a single device present significant challenges for predicting RUL. Furthermore, it is difficult to predict RUL accurately due to the randomness and unstable prediction accuracy of an individual model in different scenarios. To address these issues, this paper proposes an ensemble prediction method based on weight allocation. Consider the advantages and disadvantages of various prediction methods, Particle Filter (PF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) are selected as member models for the ensemble prediction method. The ensemble method divides observed unlabeled degradation data into a training dataset and a validation dataset, among which the training dataset trains the model, and the validation dataset is utilized to calculate the weights for each member model. Particularly, we conducted a data augmentation approach using uncertain processes to estimate the initial state parameters of the PF. A weight allocation method using distance measures is presented to assign weights to the prediction results of each member model and perform weighted prediction to obtain the final ensemble prediction results. This paper demonstrates the effectiveness of the proposed ensemble prediction method through two practical examples. Results show that the proposed prediction method has higher accuracy compared to an individual prediction model and mean weight prediction model.