A dynamic spectrum loss generative adversarial network for intelligent fault diagnosis with imbalanced data

Intelligent fault diagnosis with imbalanced data is a problem that often raises concerns. The diagnosis is more effective when the imbalanced dataset is supplemented with data augmentation methods, but there is always a gap between the real data and the generated data, especially in the frequency domain. Therefore, a dynamic spectrum loss generative adversarial network (DSLGAN) is developed for intelligent fault diagnosis. Firstly, a generative information enhancement module is built to simultaneously enhance inefficient information of the generative network from different information sources, thus creating a stable and efficient environment for the generation. Secondly, the spectrum distance is designed to find the difference in spectrum location between the real data and the generated data quantitatively by distance metric, which is used to guide the model training to generate high-quality data with similar features to the real data. Finally, the dynamic spectrum loss is proposed based on the spectrum distance to break through the synthesis of difficult frequencies in the data, by reducing the weight of easily synthesized frequencies in the spectrum while dynamically focusing on the difficult frequency components during training to achieve better generation results. In addition, experiments are conducted using several datasets, and the diagnostic accuracy of DSLGAN is 99.63% and 99.65%, reaching a very high level and verifying the effectiveness and superiority of DSLGAN.