Cross-sensor contrastive learning-based pre-training for machinery fault diagnosis under sample-limited conditions

Recently, data-driven approaches have been extensively used in fault diagnosis. However, most existing methods are based on single-sensor fault data, which is hard to suit for complex industrial systems. Extracting complementary fault features from multi-sensor monitoring data is imperative, especially under limited labeled samples. Inspired by the success of self-supervised learning in handling unlabeled data, we propose across- sensor contrastive learning-based pre-training method for machinery fault diagnosis under sample-limited conditions. In the initial pre-training phase, we introduce an innovative cross-sensor contrastive framework to capture complementary features among different sensors for enhancing the acquisition of discriminative fault features. Then, in the fine-tuning phase, a novel cross-sensor interactive attention is designed for effective feature fusion to provide amore robust feature representation. The proposed method is validated on three benchmark datasets, demonstrating superior diagnostic performance under limited labeled samples and well-adapted to different working conditions.