Multi-scale information fusion model for feature extraction of converter transformer vibration signal

Abstract

The vibration signal feature extraction model of the converter transformer has the problem of low accuracy. The main reason is that the time-related information is easily lost during training. In recent years, convolutional neural networks (CNN) have been proven to break the limits of traditional classification models. CNN has achieved remarkable results in large-scale image recognition tasks. To solve the problem of difficulty in model building and loss of time-related information, this paper proposes a multi-scale fusion feature extraction model for converter transformer vibration signal. The model includes an image generation module and a multi-scale information fusion module. The image generation module converts vibration signals into time-domain, frequency-domain, and energy feature images by calculating the Markov Transform Field and Continuous Wavelet Transform. In the multi-scale information fusion module, the attention module is introduced into the multi-parallel convolutional neural network to fuse the feature images. The proposed method can make full use of the advantages of deep learning through generating images that can represent time series from multi-scale. To evaluate the effectiveness of the model in this paper, we establish a data set based on the measured vibration signals and tested by classification experiments. The result shows the accuracy of the model state recognition in this paper is 96.15%, which is better than classic time series processing networks such as LSTM and 1D-CNN.