Abstract: Coal mine working face equipment are usually consists of several complex system modules that have strong coupling among each other. Moreover, the equipment fault mechanism is complex. Therefore, when the equipment fault prediction is carried out, it is necessary to conduct real-time monitoring of equipment operation status, environmental data and operation data so as to obtain time series data of electrical, mechanical, thermal and other parameters. A method for fault prediction of working face equipment based on time series data is proposed. Firstly, the time series alignment algorithm is used to align the collected equipment monitoring data. The time columns of monitoring data are reordered, and the time columns are the key values. Each monitoring data is filled in as the label value, and the previous value is filled in the vacant value. Secondly, the fault-related factors are selected according to the fault characterization phenomenon and the occurrence mechanism. And the correlation between the relevant factors is calculated by Pearson correlation coefficient analysis method, thereby determining the fault prediction factor set. Finally, the long short-term memory(LSTM) network is used to establish a fault prediction model for working face equipment. The normalized set of fault prediction factor set is used as the input and the fault is used as the output of the LSTM prediction model. The delay time period is introduced into the LSTM prediction model to realize advanced prediction of delay faults. The test is carried out by taking the shearer overheating trip fault as an example. Through analysis, it is found that the fault prediction factor set is {drum temperature, drum current, drum start and stop, traction temperature, transformer temperature, rocker arm temperature}. When the number of LSTM network cell layers is 10, the number of hidden cells is 10, the learning rate is 0.001, the number of iterations is 1 500, and the number of samples read per time is 120, the delay time of shearer overheating trip fault is 30 min. When the test set is used for fault prediction, the advanced prediction time is 26 min , which is 4 min shorter than the delay time, indicating that the LSTM network can effectively achieve advanced fault prediction of working face equipment based on time series data.