Channel-Time Attention Based Patch-Attribute Alignment for Zero-Shot Fault Diagnosis

Recently, zero-shot fault diagnosis has gradually attracted the attention of researchers because that some type of data cannot be obtained in advance in practical production scenarios. However, the existing zero-shot methods hardly utilize multi-channel data information and learn the potential correlation between signal patches and attributes for unseen fault classes, resulting in unsatisfactory diagnostic accuracy. To address these issues, we propose a patch-attribute alignment method based on channel-time attention for zero-shot fault diagnosis. First, a feature extraction module with channel attention is introduced to obtain the multi-channel information of signal patch. Then the feature map and the corresponding attribute vector are processed interactively by an embedding-reconstruction structure. Finally, a patch-attribute alignment module with time attention CNN is utilized to predict the attribute vector and consequently obtain the corresponding fault diagnosis label. Extensive experiments show that the proposed method outperforms baseline zero-shot fault diagnosis methods, and ablation experiments demonstrate the effectiveness of each module.