Rotating Machinery State Recognition Based on Mel-Spectrum and Transfer Learning

Abstract

During drilling into the soil, the rotating mechanical structure will be affected by soil particles and external disturbances, affecting the health of the rotating mechanical structure. Therefore, real-time monitoring of the operational status of rotating mechanical structures is of great significance. This paper proposes a working state recognition method based on Mel-spectrum and transfer learning, which uses the mechanical vibration signal’s time domain and frequency domain information to identify the mechanical structure’s working state. Firstly, we cut the signal at window length, and then the Mel-spectrum of the truncated signal is obtained through the Fourier transform and Mel-scale filter bank. Finally, we adopted the method of transfer learning. The pre-trained model VGG16 is adjusted to extract and classify the features of the Mel-spectrum. Experimental results show that the framework maintains an accuracy of more than 90% for vibration signals under minor window conditions, which verifies the real-time reliability of the method.