Remaining Useful Life Prediction via Spatio-Temporal Channels and Transformer

Remaining useful life (RUL) prediction is a key technique for the condition maintenance of mechanical equipment. Deep learning networks can fully exploit the degradation information embedded in multisensor data and have achieved great success in RUL prediction tasks. However, most of such methods only consider the temporal dependencies in sensor time series, ignoring the spatial dependencies between sensors as well as the importance of sensors and time steps simultaneously. To this end, an RUL prediction method (referred to as AGATT), integrating attention mechanisms, graph attention networks (GATs), and transformer, is proposed. The AGATT uses two parallel channels (i.e., a spatial channel and a temporal channel), each of which comprises an attention mechanism and a GAT. The spatial channel identifies important sensors and learns spatial dependencies, while the temporal one identifies important time steps and learns temporal dependencies. The temporal and spatial features extracted by the two channels are fused to form a sequence that is fed into the transformer to predict RUL. The transformer’s prediction performance is enhanced as its input contains both temporal and spatial information at each time step. A well-known turbofan engine dataset (i.e., the C-MAPSS dataset), which includes four prediction tasks, is used to verify the RUL prediction performance of the AGATT. The experimental results show that the AGATT outperforms state-of-the-art RUL prediction methods in the three prediction tasks and achieves comparable prediction results in another one. In addition, we visualize the degradation feature vectors extracted by the AGATT as well as the importance of different sensors and time steps.