MCSANet: Cross-Modal Semantic Alignment in Multi-Attribute Learning for Zero-Shot Bearing Fault Diagnosis

Zero-shot fault diagnosis (ZSFD) faces significant challenges in aligning time-series signal features and contextual semantic information. Direct projection from feature space to semantic space may suffer from domain bias, while mutual projection approaches require complex tradeoffs among multiple objective functions. This article proposes a multiattribute cross-modal semantic alignment network (MCSANet) for ZSFD. An enhanced feature extractor incorporating a conditional fault severity encoding mechanism is employed to extract discriminative fault features across multiple attributes. The time-series features, and contextual semantic information are then aligned using a novel cross-modal embedding approach, eliminating the need for complex tradeoffs among multiple objective functions. The proposed method was validated on both self-designed and open-source bearing experiments. Experimental results demonstrate that MCSANet achieves robust diagnosis performance even under nonstationary operational conditions and limited distributional diversity in the training phase. Comparative experiments confirm that MCSANet outperforms current state-of-the-art approaches.