Fault Classification of Disconnect Switches Based on Multi-Scale RGB Image Representation and SE-CNN-Attention

To address the challenges of analyzing vibration signals, overlapping fault characteristic frequency bands, and difficulties in accurately distinguishing similar faults, this study proposes a diagnostic classification scheme for GIS disconnect switch mechanical fault detection. The scheme integrates multi-scale RGB image representation with a Squeeze-and-Excitation Convolutional Neural Network Attention (SE-CNN-Attention) mechanism. First, continuous wavelet transform is employed to decouple the one-dimensional vibration signal into low-, medium-, and high-frequency physical bands, which are then mapped to the red, green, and blue channels to generate a 64 × 64 × 3 RGB time–frequency feature image, enabling three-dimensional encoding of time, frequency, and energy intensity. Based on this approach, a SE-CNN-Attention fusion model is developed. The two-dimensional CNN automatically extracts region-specific features from the time–frequency images. Concurrently, the squeeze-and-excitation (SE) attention mechanism adaptively assigns weights to enhance fault-sensitive frequency bands and suppress noise interference. The model adopts global average pooling instead of traditional fully connected layers, combined with Dropout regularization and a Softmax classifier, to achieve efficient and robust fault classification. Experiments are conducted under four operating conditions with a total of 19,800 sample groups collected. The findings indicate that the proposed approach attains an overall classification accuracy exceeding 94%, outperforming traditional support vector machine (SVM), baseline convolutional neural network (CNN), Vision Transformer (ViT), and ResNet-18 models. The SE-Attention module significantly enhances the model’s focus on key frequency band features while accelerating convergence speed and improving classification stability. This method provides reliable technical support for intelligent maintenance and fault warning of GIS disconnect switch equipment and holds significant engineering application value.