Intelligent fault diagnosis for unknown faults of rotating machinery based on the CNN and the DCGAN

The fault diagnosis model based on machine learning can only achieve accurate recognition of the fault types included in the training, but in practical applications, it is limited by the classification mechanism of the diagnosis model and cannot achieve the recognition of new faults, which is unknown faults. To address this problem, this paper proposes a fault identification method for rotating machinery based on the Convolutional Neural Networks (CNN) and the Deep Convolutional Generative Adversarial Network (DCGAN) to identify unknown faults of rotating machinery. The method first trained CNN with each known class data to build the initial diagnosis model, and trained DCGAN to obtain the discriminative network of each known class to build the confidence probability calculation model, and then the results of the initial diagnosis were corrected according to the confidence probability, and finally the intelligent diagnosis of the known and unknown class faults was realized. The analysis results of the centrifugal pump fault simulation showed that the proposed method achieved an average diagnostic accuracy of 96.16% and 91.79% for known and unknown faults, respectively.