Audio recognition method of coal machine fault based on intelligent inspection robot

With the rapid development of robot technology and the higher requirements of safe and efficient production in coal mine, underground mechanical and electrical equipment has changed from the traditional manual inspection to the robot inspection with the function of “monitoring, detection and early warning”. In this paper, sound processing and deep learning are introduced into the intelligent processing of mining system to analyze the data intelligently. The key technologies of voice preprocessing, spectrogram generation, feature extraction and classification are studied, which solve the problems that the speech features can not be described simultaneously in time domain and frequency domain, and lack of effective use of dynamic sequence information. The experimental data show that the CNN + LSTM + Softmax network based on CNN + LSTM model and caffe C + + deep learning framework can effectively improve the accuracy and robustness of abnormal sound recognition of coal mine equipment, reduce the complexity of the algorithm to adapt to the operation of the algorithm in embedded equipment, and realize the fault audio identification and diagnosis of robotic coal machine.