A BiGRU Autoencoder Remaining Useful Life Prediction Scheme With Attention Mechanism and Skip Connection

Abstract

Remaining Useful Life (RUL) prediction is one of the most common activities to ensure the reliability of a degradation system. In previous RUL prediction schemes based on RNN autoencoder, the multi-dimensional sensor data for each timestep made an equal contribution to the generation of the embedding vector during the encoding process. Besides, the single embedding vector carries the burden of decoding the entire multi-timestep information. To overcome the above shortcomings, two improvements are proposed: (1) For the embedding vectors to highlight critical timestep information, weights are assigned to each timestep information through an attention mechanism. (2) To reduce the decoding burden on a single embedding vector, a skip connection is introduced at each step of the decoding process to improve BiGRU decoding capabilities. The prognostic performance of the proposed method BiGRU-AS is evaluated on two publicly available datasets: the C-MAPSS dataset (simulation dataset) and the milling dataset (experimental dataset). Compared to the latest prediction methods, the experimental results show that the proposed method is competitive in RUL prediction for mechanical systems.