Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data

Abstract

Due to the time-varying working conditions of planetary gearboxes, the labeled data available are usually limited, current intelligent fault diagnosis methods cannot achieve satisfactory results in the case of limited labeled data. In this paper, a novel method called deep balanced domain adaptation neural network (DBDANN) is proposed for fault diagnosis of planetary gearboxes. First, the multiple convolutional layers were used to extract transferable features layer-by-layer from the raw vibration data of source and the target domain. Then, multi-layer balanced domain adaptation is applied to help training the model, which can reduce the discrepancy of the marginal probability distribution and the conditional probability distribution simultaneously. At last, the test data was fed into the model to evaluate the performance. Experiments with multiple cross-domain tasks under varying speeds and loads demonstrate the effectiveness of the DBDANN, and the proposed model obtained a better performance when compared with the state-of-the-art methods.