Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

Abstract

Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning-based diagnosis methods difficult to carry out. To solve this problem, this article proposes a new fault diagnosis framework called multilabel one-dimensional (1-D) generation adversarial network (ML1-D-GAN). In our method, Auxiliary Classifier GAN is utilized first for real damage data generation. Then the generated and real damage data are both used to train the fault classifier. Experimental results reveal that the generated data is applicable, and ML1-D-GAN improves the diagnosing accuracy for real bearing faults from 95% to 98% when trained with the generated data. The scalability of the learning model is also proven in the experiment.