Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

Abstract

Rotating machinery often runs under large-speed-fluctuation (LSF) conditions, which results in severe data distribution domain shift for intelligent fault diagnosis methods. However, this challenge is rarely discussed in current studies. Hence, motivated by the active imagination of a human being, a new tool named Actively Imaginative Data Augmentation (AIDA) is constructed to solve machinery intelligent diagnosis under LSF conditions. Two adversarial training steps, namely, knowledge learning and sample imagining, are included in AIDA. In knowledge learning, a deep model is trained to learn the classification knowledge. In sample imagining, the parameters of the deep model are fixed and samples are generated via inversely training the model. As a result, diversified samples and an intelligent deep model adapting to the LSF condition are obtained by alternately carrying out the two steps. Moreover, a detailed discussion is given to interpret the process of actively imagining samples in the proposed AIDA, in which some measures are designed and the feature visualization is conducted. Experimental results show the effectiveness and superiority of AIDA in machinery diagnosis under LSF conditions, and the good performance of AIDA is due to the diversified dataset generated by changing the degrees and directions of at each sample imagining.