Cross-Domain Acoustic Diagnosis Method of Rotating Machinery Based on Vibration and Acoustic Migration

Due to the lack of fault sample of the acoustic signal and the susceptibility of the acoustic signal to interference from reverberation and background noise, constructing an acoustic diagnostic model is difficult. Therefore, in this article, a novel migration diagnosis of different devices from vibration to acoustic (MD3VA) of the rotating machinery is proposed. First, the improved recursive least squares and the resonance-based sparse signal decomposition are used to remove the reverberation and background noise of the collected sound signal. Then, the fault component distribution model reflecting fault categories is learned by combining multisource-domain fault vibration data with the deep convolutional generative adversarial network (GAN). And the amplitude variation distribution model is learned based on the GAN. Combining the normal state acoustic signal and common characteristics distribution models of the vibration signal, the acoustic fault sample is generated by using the mechanism character generative model (MCGM). Finally, the acoustic diagnosis model is constructed based on the convolutional neural network utilizing virtual acoustic fault samples. The accuracy of the MD3VA method is validated using the experiment and the industrial data. The outcomes demonstrate that the average diagnostic accuracy of the MD3VA reaches 76.3% in various tasks, which is more than 7.13% higher than the accuracy of the comparative method.