Feedback comparison for zero-shot rolling bearing composite fault diagnosis

Abstract:

To solve the problems of fault identification in models stemming from the difficulty of acquiring composite fault data in complex industrial environments, a novel zero-shot rolling-bearing composite fault diagnosis model based on contrastive feedback generation is proposed. Initially, continuous wavelet transform is employed to convert vibration signals into time-frequency images, thus preserving the temporal and spectral characteristics of faults more effectively. Subsequently, an attention-guided ConvNeXt feature extraction module is introduced, harnessing channel and spatial attention mechanisms to enhance fault feature representation, mitigate interference from extraneous information, and augment the distinctiveness of fault characteristics. Integrating adversarial training and attribute feedback alignment networks ensures that the generated pseudo-fault features accurately reflect their corresponding fault attribute information, achieving high-fidelity fault feature generation. A contrastive learning module is incorporated to produce fault features that are proximate to positive samples while maintaining distance from other samples, thereby further enhancing the performance of the feature generator and the discriminative power of the features. Finally, calculating the similarity between the pseudo-fault features and the unknown composite fault features, the category label with the highest similarity is assigned as the label for the unknown composite fault, thereby achieving its diagnosis. Experimental results demonstrate that the feature extraction network augmented with the attention mechanism improves diagnostic accuracy by 8.42% compared to other networks; enhances by 14.67% over using only the WGAN-GP generation module; and significantly elevates fault diagnosis accuracy by 28.67% compared to other models, thereby validating the effectiveness and superiority of the proposed model, offering an innovative solution for the intelligent maintenance of mechanical equipment.