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摘要
With the rapid development of new-generation artificial intelligence technology, intelligent fault diagnosis methods have garnered considerable attention, finding various applications in diverse industry scenarios, such as aerospace, ocean engineering, and automobile industries. However, there is a lack of sufficient fault samples for effectively training an intelligent fault diagnosis model. This shortage may affect the stability and reliability of this model. Furthermore, the existing intelligent diagnosis methods for small fault samples require highly correlated measured data sets. Therefore, this paper proposes an intelligent fault diagnosis method for industrial equipment that utilizes small fault samples driven by digital data and feature enhancement. First, a virtual model of the equipment is constructed and updated using the vibration mechanism knowledge and the measured data with the health state. Subsequently, digital fault data featuring typical fault modes are generated using the virtual model. The characteristics of the digital fault data are enhanced using state-of-the-art artificial intelligence algorithms, such as generative adversarial networks. The enhanced data are then used to train an intelligent fault diagnosis model based on convolutional neural networks. Finally, the feasibility of the proposed method is verified using a case study of vehicle transmission. The results suggest that the proposed method can provide a promising solution for intelligent fault diagnosis of modern equipment in practical industrial applications under small fault samples. ? 2023 Chinese Academy of Sciences.
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