Digital Twin-assisted Gearbox Fault Diagnosis Framework using Hybrid Adversarial Domain Discriminative Networks

In industrial applications, fault diagnosis of gear-boxes is critical for ensuring the reliability and safe operation of machinery and equipment. However, data-driven fault diagnosis techniques rely heavily on high-quality labeled data for training, which is often difficult to obtain in real industrial scenarios. To address this challenge, this paper presents a digital twin fault diagnosis framework. First, the digital twin technique is utilized to build a virtual model of a gearbox that generates sufficient labeled fault data. Second, we develop a hybrid adversarial domain discriminative network, which incorporates a spatial channel reconstruction convolution module instead of a conventional residual network to enhance the model's feature extraction capabilities. Additionally, we propose a dynamic weighted joint distribution adaptation method to improve domain confusion capability, minimizing the granularity of features between the two types of data. Finally, the effectiveness and reliability of the proposed method are validated through experiments.