Contrastive regularization guided label refurbishment for fault diagnosis under label noise

Abstract

Conventional intelligent fault diagnosis studies require precise labels by default. However, label noise is inevitable in real industrial environments due to human errors, measurement biases, data transmission and storage errors. The performance of existing data-driven methods may be severely affected by mislabels. In this paper, we investigate the fault diagnosis issue of rotating machinery under noisy labels. A generalized framework contrastive regularization guided label refurbishment (CRLR) is proposed, which utilizes contrastive regularization function extracting class prototype to guide label refurbishment. The learned contrastive representations can suppress the misleading effect of noisy labels. The theoretical analysis from an information theoretic perspective also ensures the effectiveness of CRLR. Validation experiments have been conducted on public and private collected datasets using the ResNet-50 as the backbone network. The experiments demonstrate that our proposed framework outperforms the current state-of-the-art methods at different noise rates.