Similarity-based deep learning approach for remaining useful life prediction

Abstract

Abstract Remaining useful life (RUL) prediction has been a difficult problem in industry and academia. Deep learning techniques based on supervised learning have made great progress compared to traditional prediction algorithms. However, due to the random nature of system degradation behavior, deep supervised learning cannot accurately adapt the model obtained based on the training set to the systems in the testing set. The similarity-based prediction method has achieved accurate approximate remaining life, which called similar RUL. Therefore, the combination of supervised learning and similarity-based prediction has the potential to take advantages of the learning ability of the network model and similar RUL to improve the prediction accuracy. This paper investigates the effect of using similarity to improve the prediction accuracy of deep supervised learning. In addition, since health indicator (HI) and target labels are the essential factors of supervised learning, this paper proposes a method for constructing HI based on unsupervised learning. Then according to the constructed HI, a difference-based method proposed to generate target labels. To verify the effectiveness of the proposed method, this paper conducted experimental verification on the C-MAPSS engine dataset and XJTU-SY bearing dataset and made related comparative experiments. Experimental results show that the proposed method obtains favorable results against the traditional methods.