Fault diagnosis of three-phase inverter based on GAF-CNN

Abstract

To apply the advantages of deep learning in recognizing two-dimensional(2D) images to three-phase inverter fault diagnosis, a three-phase inverter fault diagnosis model based on gramian angular field(GAF) combined with convolutional neural network(CNN) was proposed. Since the current signals of the inverter in different working states are different, the images formed by the time series encoding are also different, which enables the image recognition technology to be used for time series classification to identify the fault current signal of the inverter. Firstly, the one-dimensional(1D) inverter fault current signal was converted into a 2D image through the GAF. Next, the CNN model suitable for inverter fault diagnosis was input to realize the detection, classification and location of inverter fault. The simulation results show that the recognition accuracy of this method is 99.36% under different noisy data. Compared with other traditional methods, it has higher accuracy and reliability, and stronger anti-noise interference capability and robustness in dealing with noisy data. Therefore, it is an effective fault diagnosis method for inverters.

Keywords

fault diagnosis / gramian angular field(GAF) / convolutional neural network(CNN) / anti-noise interference / robustness

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