Contrastive learning fault diagnosis method for winder chuck based on adaptive sample augmentation and pseudo-label correction

In the chemical fibre production process, the winding stage has a significant impact on the final fibre quality. Ensuring accurate and reliable fault diagnosis of the winder is crucial for maintaining production efficiency. However, current fault diagnosis of winder chucks faces the challenge of a lack of effectively labelled fault data. Contrastive learning, as an effective solution, has been gradually applied. However, existing contrastive learning frameworks have not been specifically designed for vibration signals of rotating equipment, where spindle vibration signals exhibit high similarity between samples and strong background noise. To address these challenges, this paper first proposes a multi-layer wavelet attention convolutional signal adaptive sample augmentation method to generate diverse positive sample pairs, in order to accommodate the actual working conditions of vibration signals. Secondly, a progressive pseudo-negative sample correction contrastive learning method is proposed to resolve the sampling bias caused by incorrectly treating samples with the same actual label as negative samples. Experimental results demonstrate that the proposed method outperforms existing methods in fault diagnosis accuracy. Further diagnostic experiments on vibration signals from a winder in a chemical fibre plant validate the strong adaptability of the proposed method in real-world operational scenarios.