A Transferrable Data-Driven Method for IGBT Open-Circuit Fault Diagnosis in Three-Phase Inverters

Abstract

Machine learning (ML) based data-driven methods have shown promising performance in power converter fault diagnosis. However, the existing ML model trained by one fault database can only work for the corresponding converter system, but cannot work accurately for a different system with the same topology but different parameters. In this article, a novel transferrable data-driven fault diagnosis is proposed for insulated gate bipolar transistor open-circuit fault diagnosis in three-phase inverters. First, three-phase current signals are sampled as original inputs and fault features are generated by manifold feature learning. Then, an extreme learning machine model is trained by the data from the source system to form an initial diagnostic model. After that, a model adaption process is designed to adjust the model's parameter by minimizing the distribution divergence between the data from the source and target systems. As the biggest advantage, one trained diagnostic model can be applied for different systems without the need for multiple training processes. Finally, offline tests and real-time experiments are conducted to verify the performance of the proposed transferrable method.