Time–frequency feature-level independence contrastive learning with semantic consistency learning for mechanical fault diagnosis

Abstract

In modern industrial systems, vibration signals acquired by sensors are frequently subject to varying degrees of distortion and degradation. Furthermore, the high cost of annotation in industrial settings leads to a severe scarcity of labeled fault samples. In recent years, self-supervised contrastive learning shows great promise for fault diagnosis under limited labeled data. However, most existing methods rely on instance-level contrastive learning, resulting in insufficient inter-class discriminability in practical applications. Additionally, they fail to effectively integrate time–frequency complementary information with the structured semantics of soft labels, restricting representation capacity and generalization. To address these issues, this paper proposes a self-supervised framework called time–frequency feature-level independence contrastive learning with semantic consistency learning. It combines multi-scale feature extraction with multi-level collaborative optimization to learn discriminative representations from unlabeled vibration data. Specifically, we first design a multi-scale feature extraction module to extract and coordinate local time–frequency and global features. Within the multi-level collaborative optimization module, we introduce a time–frequency feature-level independence contrastive learning component. It effectively refines the feature representations and enhances their discriminability. Furthermore, we integrate a semantic consistency learning module to strengthen semantic constraints, boosting the model’s robustness and generalization under data distribution shifts. Extensive experiments on three public datasets demonstrate that the proposed method can efficiently extract generalized feature representations from unlabeled vibration signals and achieves outstanding diagnostic performance. The source code will be available later at https://github.com/szq0816/TFFICL-SCL.