Fault diagnosis of wind turbine gearboxes using a temporal contrastive learning model integrated with multi-kernel Shiftwise convolution
TL;DR
A temporal contrastive framework integrating a hybrid multi-kernel Shiftwise convolutional network, optimizing feature spaces without extensive manual annotation is presented, substantially outperforming existing methods.
Abstract
Sustained wind turbine gearbox operation under volatile conditions and intense background noise complicates vibration signals, rendering reliable fault label acquisition cost-prohibitive. Under scarce labeling constraints, traditional data-driven methodologies frequently fail to isolate distinctive fault signatures. To circumvent this, this paper presents a temporal contrastive framework integrating a hybrid multi-kernel Shiftwise convolutional network, optimizing feature spaces without extensive manual annotation. Crucial to this framework is a multi-kernel Shiftwise architecture; by marrying channel-shift mechanisms with lightweight parallel convolutions, it captures multi-scale local anomalies efficiently, neutralizing traditional receptive field limitations. Concurrently, a dual-level self-supervised paradigm extracts both localized multi-scale features and global contextual representations directly from unlabeled field data. Downstream gearbox state classification is subsequently achieved by fine-tuning the pre-trained encoder with sparse labeled samples. Validation on the BJTU gearbox dataset and SUFD yields diagnostic accuracies of 90.84% and 91.40% using merely 2.5% and 5% labeled data, substantially outperforming existing methods.