A Transferable Deep Learning Network for IGBT Open-circuit Fault Diagnosis in Three-phase Inverters

While data-driven methods start to be applied to fault diagnosis of power converters, there are still some limitations: (1) feature extraction relies on expert experience, (2) the model trained in one system cannot be applied to another different system, and (3) abundant fault data is difficult to obtain in practical applications. To address them, a transferable deep learning network for insulated bipolar gate transistor (IGBT) open-circuit fault diagnosis is proposed in three-phase inverters. First, the lightweight convolutional neural network (CNN) is constructed to automatically extract features from the original current signals and complete the operation condition identification. Then, the designed network is pre-trained with data from the source domain (simulation model). After that, a transfer learning strategy is designed to fine-tune the network by using a few data samples in the target domain using real-time hardware in the loop. Both simulation and hardware-in-the-loop results demonstrate the effectiveness of the proposed method with 99.52% and 98.30% diagnostic accuracy, respectively.