Multivariate multi-step time series prediction of induction motor situation based on fused temporal-spatial features

Induction motor temperature situation prediction provides a decision basis for preventive maintenance in coal mining companies. However, multi-step prediction of induction motor temperature is a challenge due to the complexity of working conditions and external disturbances in surface coal mines. This paper proposes a multisensor fusion multi-step prediction model based on Graph Convolutional Neural Network with Long Short-Term Memory Network (GCN-LSTM). Specifically, the model takes into account the spatial correlation and long-term temporal dependence of multi-source sensors as well as the temporal-spatial fusion correlation at different times. This thesis is based on multi-source temperature sequence data collected from a mining induction motor. Experimental results show that the model is able to achieve 31.3%, 38.7%, and 17.1% performance improvement compared to CNN, LSTM, and GCN methods.