Machinery New Emerge Fault Diagnosis Based on Deep Convolution Variational Autoencoder and Adaptive Label Propagation

In the research field of mechanical equipment fault diagnosis, usually only the existing fault types are identified, and the new emerge class of the fault is ignored, however, the new emerge fault class may also occur actually. In order to solve the problem, a novel fault diagnosis model based on deep convolution variational autoencoder network and adaptive label propagation (DCVAN-ALP) is proposed. Firstly, the initial high dimensional features are constructed by using the double tree complex wavelet packet method as the input of the model. Secondly, the convolutional neural network architecture is applied to construct the variational autoencoder, and the local and non-local characteristics of samples are embedded into the loss function for training, which is considered to improve the identification of hidden layer features of the neural network. Finally, t-SNE and the improved label propagation algorithm are adopted to process the hidden features of the neural network, which can achieve the purpose of diagnosing the existing fault class and especially new emerge fault class. Experimental results show that the proposed model can effectively extract the fault characteristics of the vibration signal, and it also has a significantly higher recognition accuracy rate than other typical deep learning methods and traditional classifiers in diagnosing new emerge fault class.