Real-time Diagnosis based on Signal ConvolutionPooling Processing and Shared Filter Learning for Transistor Open-Circuit Faults in a T-type Inverter

This paper proposes a data-driven method based on signal convolution pooling for real-time fault diagnosis in T-type inverters. The model is composed of an auxiliary neural network and a multilayer convolution feature classifier (MCFC). The auxiliary neural network can learn and provide filter parameters for an MCFC by learning from a small training dataset. Through shared filter learning and a global average pooling layer, a feedforward MCFC can greatly reduce testing time. This makes the approach suitable for real-time fault diagnosis. A feature processing function is used to retain fault features observed in measured three-phase current signals while avoiding effects of load changes. A multi-signal sequence reconstruction strategy is proposed to transform multiple time-series diagnostic signals into an input feature map for the MCFC. This strategy extends the domain of the MCFC information by increasing the input channel count of the auxiliary neural network. The combined approach increases fault diagnosis accuracy compared to prior work. The performance of the proposed diagnosis method is validated with experiments.