LSTM-Based Multi-Task Method for Remaining Useful Life Prediction under Corrupted Sensor Data
Kai Zhang, Ruonan Liu
Abstract
Data-driven remaining useful life (RUL) prediction plays a vital role in modern industries. However, unpredictable corruption may occur in the collected sensor data due to various disturbances in the real industrial conditions. To achieve better RUL prediction performance under this situation, we propose a novel multi-task method for RUL prediction, which is named multi-task deep long short-term memory (MTD-LSTM). In MTD-LSTM, convolutional neural network (CNN) and long short-term memory (LSTM) are first employed for feature extraction and fusion. Then, the extracted features are fed into the multi-task learning module, which contains missing value imputation and RUL prediction module. The missing values imputation task and RUL prediction task are performed simultaneously. The purpose of the missing value imputation is to obtain integral degradation information by recovering the complete data; thus, the RUL prediction task performs better under corrupted sensor data. In addition, a novel loss term is proposed to smooth the RUL prediction results without any manual post-processing. The effectiveness of the proposed method is verified on the simulated dataset based on the C-MAPSS dataset.