Dynamic Simulation Model-Driven Fault Diagnosis Method for Bearing under Missing Fault-Type Samples

Existing generative adversarial networks (GAN) have potential in data augmentation and in the intelligent fault diagnosis of bearings. However, most relevant studies only focus on the fault diagnosis of rotating machines with sufficient fault-type samples, and some rare fault-type samples may be missing in training in practical engineering. To address those deficiencies, this paper presents an intelligent fault diagnosis method based on the dynamic simulation model and Wasserstein generative adversarial network with gradient normalization (WGAN-GN). The dynamic simulation model of bearing faults is constructed to obtaining simulation signals to replace and complement the missing fault samples, which are combined with the measured signals as training data and then input into the proposed WGAN-GN model for expanding and enhancing the data. To test the effectiveness of the simulated samples, a fault classification model constructed by stacked autoencoders (SAE) is used to classify the enhanced dataset. According to the results, the proposed model performs well when used to diagnose faults under missing samples and is preferable to other methods.

Automated summary

This paper proposes an intelligent fault diagnosis method based on a dynamic simulation model and WGAN-GN. The simulation signals are used to replace missing fault samples and combined with measured signals for training data input into the proposed WGAN-GN model to expand and enhance the data. The effectiveness of the simulated samples is tested using a fault classification model constructed by stacked autoencoders (SAE), and the results show that the proposed model performs well in diagnosing faults under missing samples and outperforms other methods.