Transformer fault diagnosis method based on multilevel acoustic information

Abstract

To more accurately obtain the feature information embedded in the acoustic pattern of transformers, a transformer fault diagnosis method is proposed based on multilevel acoustic information of 14 state types. In this method, a parallel dual-channel fault diagnosis model, CNN-BiLSTM-Transformer, is established. First, the modified Mel inversion coefficients and Mel spectrograms are extracted from the original acoustic pattern data. The modified Mel inversion coefficients and Mel spectrograms are then input into the parallel dual-channel model. In the first channel, a convolutional neural network model is used to extract the feature information of maps. In the second channel, a bidirectional long- and short-term memory network and a Transformer encoder are used to partially extract the temporal features in the MFCCs. Finally, the temporal features extracted from the two channels are fused through multimodal fusion for training. The experimental results show that the proposed diagnostic method can achieve an average accuracy of 99.5% in multiple fault diagnosis. Compared with current mainstream acoustic single-channel diagnostic models, the diagnostic rate of this model is improved by an average of 4.8%, exhibiting higher accuracy and robustness.