A Fault Diagnosis Method for Inverter Based on Data Augmentation with IGA Specific Coefficient Wavelet Reconstruction

To solve the problem of insufficient fault samples for inverter fault diagnosis in weakly supervised small sample scenario, this paper proposed a fault diagnosis method for inverter based on data augmentation (DA) with improved genetic algorithm-specific coefficient wavelet reconstruction (IGA-SCWR). First, IGA is used to find the optimal fine-tuning coefficient (FTC) k, which is the key to ensure the quality of DA. Second, through wavelet packet decomposition, the wavelet coefficients are changed under the guidance of the optimal FTC k, and sufficient samples of new synthesized data were obtained. Then the “end-to-end” feature extraction and fault classification are realized based on convolutional neural networks (CNNs). Finally, the effectiveness of the proposed method is verified by a series of experiments.