ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis

Abstract Data-driven algorithms for bearing fault diagnosis have achieved much success. However, it is difficult and even impossible to collect enough data containing real bearing damages to train the classifiers, which hinders the application of these methods in industrial environments. One feasible way to address the problem is training the classifiers with data generated from artificial bearing damages instead of real ones. In this way, the problem changes to how to extract common features shared by both kinds of data because the differences between the artificial one and the natural one always baffle the learning machine. In this paper, a novel model, deep inception net with atrous convolution (ACDIN), is proposed to cope with the problem. The contribution of this paper is threefold. First and foremost, ACDIN improves the accuracy from 75% (best results of conventional data-driven methods) to 95% on diagnosing the real bearing faults when trained with only the data generated from artificial bearing damages. Second, ACDIN takes raw temporal signals as inputs, which means that it is pre-processing free. Last, feature visualization is used to analyze the mechanism behind the high performance of the proposed model.