Transformer Voiceprint Feature Extraction and Fault Recognition Based on MFCC and Deep Learning

This study proposes a transformer fault voiceprint recognition model based on Mel Frequency Cestrum Coefficients (MFCC) and deep learning, aiming to address the challenges posed by low signal-to-noise ratio and the associated issues of low accuracy and unstable performance in transformer fault recognition using transformers. The proposed model employs a two-step approach: firstly, the transformer acoustic signal is transformed into MFCC, forming a group of MFCC feature vectors as input. Secondly, convolutional neural networks (CNN) are utilized to extract spatial feature information from the MFCC, and subsequently, bi-directional Long-Term Memory (Bi-LSTM) is employed to fully extract temporal feature information. The feature classification is then performed through a fully connected layer and a Softmax layer. Experimental results demonstrate the model's stability under low signal-to-noise ratio noise background, exhibiting a fault identification accuracy rate exceeding 99.5% for transformer acoustic signals under three distinct operating conditions.