Abstract: The bearing residual life prediction of coal mine machinery equipment is of great significance for equipment maintenance.The existing bearing residual life prediction methods are either difficult to establish an accurate mathematical model of bearing failure, or the prediction precision is constrained by the sample completeness and accuracy.And the degradation characteristic quantity usually adopts time domain and frequency domain indicators, which are greatly affected by the harsh working environment of coal mine machinery equipment, resulting in low prediction precision.In order to solve this problem, a bearing residual life prediction method of coal mine machinery equipment based on empirical mode decomposition(EMD)and grey model(GM)is proposed.EMD is used to filter the vibration acceleration signal of coal mine machinery equipment bearings.The root mean square of the filtered signal is extracted as the degraded characteristic quantity representing the bearing health state so as to form the degraded characteristic quantity sequence.The GM is trained with the degraded characteristic quantity sequence, then the GM bearing residual life prediction model is established to predict the change trend of the degraded characteristic quantity, and the time interval when the degraded characteristic quantity reaches the set threshold is used as the residual life prediction value.The test bench and engineering application results show that the method can effectively predict the bearing residual life of coal mine machinery equipment with high prediction precision, and the prediction results can guide field equipment maintenance.