Multi-spectral fusion power equipment fault recognition based on prompt learning

To address the issue of weak fault recognition ability of power equipment in single-spectrum images, a multi-spectral fusion recognition method based on prompt learning is proposed. A multi-spectral imaging system is used to capture images of normal and faulty power equipment, collecting multi-spectral data including visible light, infrared, and ultraviolet. The collected dataset is annotated with text labels for training the large model. The generalization ability of the large model in power equipment fault recognition is verified, and the original large model is tested on the collected dataset for device type and fault recognition. Trainable prompts based on infrared and ultraviolet images are designed for parameter updates. Throughout the training process, the parameters of the pre-trained large model remain fixed, and only the designed lightweight prompts are updated, significantly reducing the number of training parameters and alleviating the model's dependence on large-scale datasets. The proposed method is compared with several existing methods, and the results demonstrate that this approach can greatly improve the accuracy of power equipment fault recognition, achieving an accuracy of 90.14%. Ablation experiments and visual results further validate the effectiveness of the method. Additionally, the proposed method optimizes only a small number of trainable parameters, ensuring its efficiency.