Robust Multilabel Feature Selection With Label Enhancement for Fault Diagnosis

In data-driven fault diagnosis, feature selection not only reduces model complexity but also plays a pivotal role in improving prediction accuracy. Existing studies typically employ binary labels to indicate whether a sample exhibits a specific fault, yet this approach fails to capture the severity of the fault. Moreover, features extracted from vibration signals often contain significant noise and redundancy, which adversely affects the training of models. To address these issues, we propose an alternating optimization strategy for feature space and label space. In the label enhancement stage, continuous-valued labels capturing subtle differences in label values through sample-label similarity. In the feature selection stage, a regularization constraint based on feature coefficient correlation is introduced to achieve progressive elimination of redundant and noisy features. Through iterative optimization, label enhancement and feature selection mutually reinforce each other, leading to improved diagnostic accuracy. Building upon this strategy, this article proposes a novel multilabel feature selection method, label-enhanced feature selection (LEFS). Experimental results on real-world datasets for bearing and gearbox fault diagnosis validate the effectiveness and advantage of the LEFS method.