A Deep Learning-Based Fault Diagnosis Method for Flexible Converter Valve Equipment

Long-term failures in flexible converter valve equipment pose significant risks, potentially compromising operational efficiency or leading to complete malfunction. Accurately identifying equipment faults is essential to improve overall reliability and minimize downtime. This study introduces an innovative fault diagnosis method utilizing an attention mechanism. The method integrates a lightweight model incorporating one-dimension depthwise convolutional layers for spatial feature extraction and bidirectional long short-term memory for capturing temporal dynamics. A pioneering time-channel joint attention module enhances the extraction of fault-related data from time series and channel maps. Experimental results underscore the method's efficacy in fault diagnosis under varying Gaussian noise conditions. Notably, the approach demonstrates remarkable consistency in accuracy across various experimental setups, underscoring its robust performance and potential applicability in real-world scenarios where reliability is critical. In addition, the proposed method has a moderate number of parameters and training time, indicating that the model can be embedded in front-end equipment.