Online Diagnosis Method for Open-Circuit Fault of NPC Inverter Based on 1D-DSCNN-GMP Lightweight Edge Deployment
Feng Xie, Xin Tang, Fei Xiao, Yifei Luo, Haolan Shen, Zenan Shi
Abstract
Due to the open-circuit fault of neutral-point-clamped (NPC) inverter having the characteristics of solid concealment and multiple fault modes, it is necessary to study an accurate and efficient fault diagnosis method to detect and locate the fault in time. However, the existing methods are either based on artificial analytical modeling or signal analysis, which is challenging to model and needs better portability or the model needs to be simplified to implement online applications. Therefore, this article proposes a data-driven fault diagnosis and high-performance online inference method. The diagnosis method simplifies the traditional labeling method by designing labels and takes one-dimensional depth separable convolution (1D-DSC) and global max pooling (GMP) to replace conventional 1-D convolution and local pooling called 1D-DSCNN-GMP. Then, the model is compressed and optimized based on the TensorRT framework. Experiments show that compared with the existing methods, the proposed method can reduce the number of model parameters by more than 90%. The average online inference time of the model after compression and deployment to the edge computing board (ECB) is about 1 ms, only 5% fundamental period, and can maintain 100% diagnostic accuracy, with better online application potential.