Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA model.

ABSTRACT: In response to the challenges posed by imbalanced failure diagnosis samples, limited labeled data, and significant computational costs in actual industrial production settings, this paper introduces a high-precision, low-resource, end-to-end fault diagnosis framework. On one hand, we propose a data augmentation method based on GCGAN, which combines CNN and GRU to construct core network structures for the generator and discriminator. We integrate a novel Smoothed Hinge-Cross-Entropy loss function to facilitate the training process, effectively mitigating mode collapse and vanishing gradient issues. On the other hand, we design a lightweight fault diagnosis model based on MDSCNN-ICA-BiGRU. By substituting standard convolutions with depthwise separable convolutions on deeper channels, the mod

SUBMITTER: Liu L 

PROVIDER: S-EPMC11811228 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature