Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning

Abstract

In the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in handling the long-term dependencies of time series data and mining the complexity of spatiotemporal information in the field of mine maintenance. The LODS model integrates the advantages of LSTM, DFNN and STAN, providing a comprehensive method for effective feature extraction and prediction. Through experimental evaluation on multiple data sets, the experimental results show that the LODS model achieves more accurate predictions, compared with traditional models and optimization strategies, and achieves significant reductions in MAE, MAPE, RMSE and MSE of 15.76, 5.59, 2.02 and 11.96, respectively, as well as significant reductions in the number of parameters and computational complexity. It also achieves higher efficiency in terms of the inference time and training time. The LODS model performs well in all the evaluation indexes and has significant advantages; thus, it can provide reliable support for the equipment failure prediction of the mine maintenance robot.