Contrastive BiLSTM-enabled Health Representation Learning for Remaining Useful Life Prediction

Abstract

Remaining useful life (RUL) prediction is of vital significance in prognostics health management tasks. Due to powerful learning capabilities, deep learning methods, particularly long short-term memory (LSTM) have been widely applied in RUL prediction. However, many existing deep learning approaches overlook the inherent ordered relationship between samples in the direct mapping from sliced data to RUL pattern. To capture the faithful and ordered health representation of a given system, a Contrastive Bidirectional LSTM-enabled Health Representation Learning (CBHRL) framework is proposed. Firstly, the supervised contrastive regression loss (SupCR) is implemented to extract continuous health representation. The SupCR is designed to rank the similarity among health representations from different samples, prompting them highly correlated with linear RUL label. Among the process of contrastive learning, the series odd-even decomposition (SOED) method is devised to construct multi-view degradation data, which improves generalization ability. Finally, since the health representation is constructed on basis of similarity, a new similarity prediction method is proposed as the complement of regression prediction method. Experimental results show the health representations extracted by CBHRL achieve improved ratio ranging from a minimum of 17.19% to a maximum of 291.30% in monotonicity, smoothness and trendability.