A Hybrid Model of Kalman-ARIMA-LSTM for Flow Prediction of Mine air Compressors

Abstract

As an essential power system in coal mine production, the accidental failure or downtime of mine air compressors will cause production stagnation and economic losses. The accurate and reliable flow prediction of air compressors plays a crucial role in fault diagnosis for air compressors. In this paper, we propose a hybrid model combining the Kalman filter, the autoregressive integrated moving average and the long short-term memory to predict the flow data of mine air compressors. The noise existing in the flow data of air compressors can be reduced by the Kalman filter so that the sequence can be recognized by the autoregressive integrated moving average model. Then, the long short-term memory neural network will be utilized to transform the linear data to nonlinear for conforming to the actual flow situation of air compressors. The prediction results show that the hybrid model has a good performance in forecasting the flow data of mine air compressors.