A Fault Diagnosis Method Composed of Gaussian Cloud Model and Domain-Invariant Features Extraction

Artificial intelligence technology is widely used in mechanical system fault diagnosis as an effective means, but there are few fault samples in practice, which seriously restricts the industrial application of Al diagnosis model to high-precision diagnosis. In order to overcome the lack of fault samples, a fault diagnosis method composed of Gaussian cloud model and domain-invariant features extraction is proposed for expanding fault training samples in this paper. The method include three steps. Firstly, the limited measured samples is obtained by calculating the time-domain feature indexes of original signals. Secondly, the Gauss cloud model is construct based on the measured samples, and perform the forward cloud computing to generate a large amounts of cloud drops as simulated samples. Finally, based on the cross-domain transfer learning framework, the convolutional neural network (CNN) is utilized to extract a large number of domain-invariant features from the measured and simulated samples as the expanded fault samples to train the intelligent fault diagnosis model. The experimental investigation is carried out based on the experimental gear data respectively, and the experimental results show that the proposed method can effectively expand the fault samples to train the fault diagnosis model.