A Novel use of 1-D Convolutional Transformer Neural Network Model in CHB Motor Drive Fault Diagnosis

In this work, a novel Hybrid 1-D Convolutional Transformer Neural Network is utilized for diagnosing faults in a Cascaded H-Bridge PMSM drive system. This innovative method exhibits high precision in open-circuit switch fault detection and localization for multi-class classification. The neural network model is used to assess the overall health status, locate faulty modules, faulty phase leg localization and identify individual switch faults in two different Cascaded H-Bridge topologies: one H-Bridge per phase (Topology I) and three H-Bridges per phase (Topology II) PMSM drive systems. The study showcases the prediction accuracy, reliability, and robustness of the neural network models for both topologies. In Topology I, the model achieved accuracy rates above 99% in its fault detection and localization tasks, with an average prediction time of around 1ms per sample for all considered diagnostic scenarios. Despite Topology II being more complex. the model had prediction accuracy rates exceeding 98% and an average prediction time between 1.5ms and 9ms per sample for all its considered diagnostic scenarios. Both drive system topologies resulted in minimal memory usage of approximately 2MB and 2.1MB, respectively.