A synchronization-induced cross-modal contrastive learning strategy for fault diagnosis of electromechanical systems under semi-supervised learning with current signal

Electromechanical systems is widely employed in the manufacturing industry, with fault diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit distinct fault features, but their acquisition is subject to various limitations. Conversely, current signals, while easily measurable, typically manifest weak fault features. Therefore, selecting a signal for fault diagnosis necessitates a trade-off among cost, performance, and feasibility. To overpass these obstacles and enable flexible fault diagnosis in complex environments, this paper presents a novel contrastive vibration-current (CVC) framework that leverages synchronization information from multiple modalities to enhance the performance of single-modality models, primarily the current model. This allows the choice of any signal for monitoring during the deployment phase. Specifically, we first preprocess current signals using Clarke transformation to highlight their fault information. Subsequently, the vibration model employs semi-supervised learning to make full use of labeled and unlabeled samples. Additionally, a noise-resistant augment Mean Teacher is incorporated to enhance the robustness of the vibration model. Then, using synchronization-induced cross-modal contrastive learning (SICMCL), vibration and current features are aligned. And at the decision level, the current model leverages pseudo-labels derived from vibration. The results of experiments demonstrate that CVC excels when relying solely on single-modal signals, owing to the effectiveness of SICMCL. Moreover, for the current model, SICMCL is more beneficial in improving performance than true labels.