An Online Open Switch Fault Diagnosis Method for the DAB Converter Based on Multimodal Fusion Neural

Machine learning approaches are available for the fault diagnosis issues of the dual active bridge (DAB) converter. However, influenced by noise and multimodal diagnostic signals, the data-driven methods are still in the initial phase for certain fault diagnoses of the DAB converter. To fill this gap, a multimodal fusion neural controlled differential equation (MFNCDE) is presented to detect and locate the DAB converter’s fault diagnosis in early phases. An improved low-rank matrix fusion (LMF) method is employed to fuse the multimodal signals (voltage and current signals) of the DAB converter. The neural controlled differential equations (NCDEs) with sparse transformer (ST) extract significant information from the diagnostic signal, based on which the fault classifier obtains the fault state of the DAB converter. The datasets are collected from a real DAB converter under single-phase shift control. Contrastive experiment results demonstrate that the presented method is superior to other fault diagnosis methods.