An intelligent fault diagnosis method based on data enhancement by multi-information driven spiking generative adversarial network

Abstract The machine always operates in a normal state. Therefore, the data-driven intelligent fault diagnosis methods still have more problems, such as small samples and overall imbalance in the era of big data. The main method to address these problems is oversampling. However, the common oversampling method, including data scaling, noise addition, and even some data-generating methods, contains similar feature information. This feature information originates from a single confirmed data, such as a single direction time-domain vibration data, which makes the new data lack multi-scale and multi-perspective feature representations. Thus, this paper proposes a multi-information driven spiking generative adversarial network (MI-SpikingGAN). Specifically, its ability to learn the instantaneous and cumulative temporal features of data can be enhanced by the spiking neurons. Both the multi-domain inconsistency and peak deviation of generated data are mitigated by introducing low-frequency wrapping loss and feature-matching loss into the generator. Moreover, the feature-level data fusion is performed by using a global multi-head channel attention mechanism on the supporting data. These fused data are used to guide the discriminator in identifying as complementary, which avoids the shortcomings of single-modal input. This paper validates its performance in some opening datasets, and the experiment results indicate that the accuracy of the classifier, which was trained with the data enhanced by the MI-SpikingGAN, reaches 98.83% and is 9.11% higher than that of softmax trained by the original data. Compared to the accuracy of other methods, it is at most 6.16% and at least 2.7% higher.