Abstract
In modern industrial settings, the datasets available for mechanical fault diagnosis are often limited in size and exhibit significant imbalance posing a challenge to both the accuracy and stability of diagnostic processes. A particularly difficult task is the detection and localization of surface defects in textured materials. The complexity arises due to variable textures and the frequent scarcity of defective samples for preliminary testing. Conventional defect detection methods used in industry are typically complex, labor-intensive, and heavily dependent on expert knowledge due to the manual extraction of features and the complexity of the processing pipeline. To address these issues, the paper introduces an innovative imbalanced fault diagnosis approach leveraging the Fuzzy Clustering Generative Adversarial Network (FCGAN) model. The FCGAN is an advancement of the traditional Generative Adversarial Networks (GAN) which integrates fuzzy clustering into the discriminator’s training process for unsupervised learning. The integration of fuzzy clustering enhances the generative network’s performance. The model efficiency is further augmented by the feature extraction capabilities of FusionNet which undergoes structural optimization and conditional augmentation to generate diagnostic samples for machine faults. The effectiveness of the model is validated using two publicly accessible datasets i.e., DAGM 2007 synthetic and CCSD-NL Magnetic-Tile-Defect. The proposed method which is tested on two famous datasets, including DAGM 2007 synthetic and CCSD-NL Magnetic-Tile-Defect, has achieved prediction accuracy of 95.21% and 96.24% with a standard deviation of 65.78% and 57.44%, respectively. The results have been compared with other DL and traditional methods, including GAN, CNN, and adaptive deep CNN. The comparisons show that the proposed FCGAN data-driven fault diagnosis method has achieved significant improvements.
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Funding
This study was funded by the 2024 Jilin Provincial Department of Education Scientific Research Project: Innovative Research on the Cultivation of Product Design Talent in Application-Oriented Universities under the Background of Artificial Intelligence (Contract No.: JJKH20241628SK).
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Wang, Y., Xue, Q. Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model. Soft Comput 28, 3725–3742 (2024). https://doi.org/10.1007/s00500-024-09636-9
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DOI: https://doi.org/10.1007/s00500-024-09636-9