Time domain correlation entropy image conversion: A new method for fault diagnosis of vehicle-mounted cable terminals

Abstract

The identification of partial discharge (PD) in cable terminals is crucial for the safe operation of trains. However, the complexity of the operational environment and the similarity of PD signals make defect identification challenging. Consequently, this paper proposes a Time-domain Local Correlation Entropy Image (T-LCEI) transformation method, which constructs an entropy matrix to convert raw PD signals into images. These images embed feature and bandwidth information from the original PD data, significantly enhancing the ability to differentiate between similar PD signals. Furthermore, the method combines a Dual Attention Convolutional Neural Network (DA_CNN) for the effective classification of correlation entropy images. Experimental results demonstrate that this approach achieves an average classification accuracy of 99.69% across four typical PD defect datasets, with a testing accuracy of 97.75% in practical scenarios. Compared to existing PD detection methods, T-LCEI offers significant improvements in effectiveness and discriminability. The integration of DA_CNN further enhances recognition accuracy. The study demonstrates that the proposed method excels in PD defect identification, providing reliable technical support for on-site fault detection and maintenance, thereby significantly improving the operational safety of cable terminals.