IF4FD: Multiscale Information Fusion for Zero-Shot Industrial Fault Diagnosis

Fault diagnosis aims to identify faults occurring in industrial production processes to prevent personnel injuries and economic losses. However, there are two main challenges in solving fault diagnosis, i.e., extracting discriminative features from limited sensor data and recognizing new classes of faults. To fill these research gaps, we propose a zero-shot fault diagnosis framework, called IF4FD, based on multiscale information fusion. First, we enhanced raw data from the perspectives of category knowledge, attribute knowledge, and feature knowledge. Then, by drawing on zero-shot learning (ZSL), we can transfer knowledge of trained faults to new classes of faults, enabling the classification of previously unknown faults. The multiscale informative knowledge effectively facilitates knowledge transfer and fault classification, thereby enhancing the accuracy of zero-shot fault diagnosis. Extensive experiments on two industrial fault diagnosis datasets validate the effectiveness of the proposed method, which consistently achieves superior performance compared to representative zero-shot fault diagnosis methods, general ZSL baselines, and several supervised classifiers. A case study on real industrial data from the Cranfield Multiphase Flow Facility also confirms the method’s effectiveness in practical applications.