Certainty From Uncertainty: Multigranularity Labeling Inspired by Quantum Collapse for Learning With Noisy Labels in Fault Diagnosis

Deep learning has demonstrated exceptional performance in fault diagnosis tasks that rely on large-scale datasets. However, the high cost of annotating such datasets has led to the emergence of various automatic annotation methods. While these methods reduce labeling costs, they inevitably introduce noisy labels, which pose significant challenges to the generalization and accuracy of deep learning-based diagnostic models. Although Learning with Noisy Labels (LNL) methods mitigate the adverse effects of noisy labels through strategies such as sample separation or label correction, many rely heavily on their own prediction results to guide subsequent training, which often introduces confirmation bias, limiting the effectiveness of the trained models. To address this limitation, this article draws inspiration from quantum collapse and proposes a novel LNL strategy named Multigranularity Labeling (MgL). By integrating observed labels, pseudolabels, and collapsed labels, MgL constructs the multigranularity labels, designed to suppress confirmation bias and improve the model's tolerance to noisy labels. Extensive experiments validate the effectiveness and superiority of MgL, particularly on training datasets with high noise intensity, such as those with 90% symmetric noise. This advancement offers promising opportunities for applying datasets with lower annotation costs in real-world scenarios, ultimately contributing to intelligent diagnostic systems.