Remaining useful life prediction of mechanical equipment based on time-series auto-correlation decomposition and CNN

Abstract

Abstract Remaining useful life (RUL) prediction can provide critical information for complex equipment health states (HSs) assessment. Historical long-term HS degradation trends and current short-term HS changes are two key factors affecting RUL prediction. However, most existing deep learning-based RUL prediction methods only consider learning short-term HS change features but ignore learning long-term HS degradation trend features, which limits to improvement of RUL prediction performance. To address this problem, this paper develops a RUL prediction framework based on a combination of time-series auto-correlation decomposition (TSACD) and convolutional neural network (CNN), which can learn both long-term and short-term features of mechanical equipment, so that achieves more robust and accurate RUL prediction. First, a novel TSACD method is proposed to extract historical long-term features from collected long-term monitoring data. The advantage of TSACD is to highlight the true signal by reinforcing periodic features through the Auto-Correlation mechanism and to separate pure trend components using a deep time-series decomposition architecture. Second, the long-term features are mapped to the same space as the short-term HS monitoring data using a group linear layer, which is intended to be aligned and fused with short-term monitoring data. Third, the fused features are fed into a CNN for RUL prediction. Finally, a series of comparison experiments on the C-MAPSS dataset and the XJTU-SY dataset validate the outstanding prognostic performance of the proposed method. The experimental results show that the proposed method outperforms the other RUL prediction methods.