SVM-based Fault Detection Method for Air Conditioners in EMU Train

In this paper, we introduce and experimentally demonstrate a fault detection method for the air conditioner in the EMU train, which usually generates abnormal sounds when the faults occur. The technique utilizes the Support Vector Machine (SVM) to recognize the time-frequency characteristics of the sound generated by the air conditioner, which is extracted using the short-time Fourier transform (STFT). In addition, a detection system is built for the experiment, in which an FPGA and an embedded system are employed for sound acquisition and acoustic signal process, respectively. The penalty factor of the SVM model is optimized and accuracy of 0.977 is achieved. Our method will improve the efficiency and accuracy of fault detection for the air conditioner in the EMU train.