Short-time consistent domain adaptation for rolling bearing fault diagnosis under varying working conditions

Although traditional deep learning improves the accuracy of intelligent fault diagnosis, it suffers from a problem, which is that a change in working conditions may reduce the diagnostic accuracy. The reason for this phenomenon is that a change of working conditions influences the probability distributions. To solve this problem, domain adaptation is adopted to perform intelligent fault diagnosis. However, the design of regularization methods, such as maximum mean discrepancy (MMD), neglects the phenomenon of fault extension. Considering the property of fault extension, the paper sums up a concept named short-time consistency which means that 'during stable operation, a failure does not expand over a short time period.' Moreover, short-time consistent regularization is proposed to ensure that the output of the model meets the requirement for short-time consistency, and closed-set regularization is proposed to further solve the problem of 'types of label drop' when short-time consistent regularization is used. When the problem occurs, the number of predicted label types in the target domain is smaller than that in the source domain in the closed-set domain adaptation. Two types of regularization, namely entropy-based regularization and regularization based on the L2 norm, are easily adopted in the final loss function. The proposed method is verified by experiments.