Existing intelligent fault diagnosis methods commonly rely on extensive and balanced dataset. However, the collection of fault samples significantly lags behind that of normal samples in practical industrial scenarios, resulting in data imbalance problem that severely impacts diagnostic accuracy. To address this challenge, this paper presents a novel approach, termed the Hybrid Distance Generative Adversarial Network with gradient penalty (HDGAN-GP). Initially, a stacked autoencoders (SAE) is incorporated into the original generator to form an auxiliary generator, facilitating the production of high-quality samples. Subsequently, a loss function is devised based on a hybrid distance metric comprising cosine similarity and maximum mean difference, supplemented by a gradient penalty term to ensure stable model training. Finally, experimental validation is conducted using gear dataset. Comparative analysis with existing generative adversarial network models demonstrates that the proposed method generates superior quality fault samples, effectively addressing the challenge posed by data imbalance in fault diagnosis.
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