Fault detection method for belt conveyor idler

The existing fault detection methods for belt conveyor idler have the problems of low recognition precision, poor anti-interference capability and inability to operate stably over a long period of time. In order to solve the above problems, a fault detection method for belt conveyor idler based on time-frequency-MFCC(TFM) and multi-input one-dimensional convolutional neural network (MI-1DCNN) is proposed. Firstly, the pickup collects the audio signal of the coal conveyor idler running along the line. The dB 4 wavelet unbiased risk estimation threshold noise reduction method is used to preprocess the signal to eliminate the background noise and improve the signal-to-noise ratio. Secondly, the time domain, frequency domain and Mel frequency cepstrum coefficient (MFCC), and the first and second order difference coefficient of the noise reduction audio signal are normalized respectively, and finally assembled to obtain the feature TFM. Finally, that TFM signals are input into a MI-1DCNN model with a multi-scale c