Belt conveyor roller fault audio detection based on the wavelet neural network

Belt conveyor is the main equipment in coal production department. And it is of great significance to its normal operation on coal safety production. At present, the belt conveyors fault detection method is still not perfect. Both the discovery and identification of the belt conveyors faults are also not timely. This paper focuses on roller fault sound audio analysis, exploring a new kind of automatic fault detection and identification method based on wavelet transform and BP neural network technology, through de-noising method to extract fault feature sound improving system recognition accuracy. This method through field verification in the mine achieves good results, which proves its feasibility.