Fault Diagnosis of Rotor-bearing System under Time-varying Speeds by Using Dual-threshold Attention-embedded GAN and Small Samples

doi:10.3901/JME.2023.12.215

Abstract

Abstract: End-to-end fault diagnosis of rotor-bearing system under time-varying speeds using a few samples is challenging. Despite generative adversarial network (GAN) provides a way to solve the problem of small-sample fault diagnosis, it still has some limitations, such as gradient vanishing, weak extraction of global correlation features, and low training efficiency. Therefore, a dual-threshold attention-embedded GAN is proposed for generating high-quality infrared thermal (IRT) images to solve small-sample fault diagnosis of rotor-bearing system under time-varying speeds. First, Wasserstein distance and gradient penalty are combined to design the new adversarial loss function to avoid gradient vanishing. Second, attention-embedded GAN is constructed to guide learn global thermal-correlation features of the IRT images. Finally, dual-threshold training mechanism is developed to further improve the generation quality and training efficiency. The proposed method is used to analyze the collected small IRT images of a rotor-bearing system, and the results show that the proposed method can accurately diagnosis different fault modes using small samples under time-varying speeds, which is superior to other popular GANs.

Key words: dual-threshold attention-embedded GAN, fault diagnosis, time-varying speeds, small infrared thermal images, rotor-bearing system

CLC Number:

TH17

HAO Haidong, LI Wei, LIU Yi, YANG Bin. Fault Diagnosis of Rotor-bearing System under Time-varying Speeds by Using Dual-threshold Attention-embedded GAN and Small Samples[J]. Journal of Mechanical Engineering, 2023, 59(12): 215-224.

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