An Abnormal Detection Method of Electric Power Equipment Based on Contrast Learning

With the widespread application of Industrial Control Systems (ICS), equipment anomaly detection has become crucial for ensuring safety and reliability. Traditional detection methods rely on predefined rules and thresholds, which often lack accuracy and adaptability when faced with complex network attacks and equipment failures. To address this issue, this paper proposes an anomaly detection method based on contrastive learning. By incorporating data augmentation techniques (such as timestamp perturbation, field masking, noise addition, etc.), the method reduces reliance on large amounts of labeled data and enhances model robustness. Additionally, deep neural networks (such as CNNs and Transformers) are used for feature extraction, enabling the model to capture more complex anomaly patterns. The paper also combines classification loss with contrastive loss, improving the model's ability to distinguish between normal and abnormal samples in the feature space, thus enhancing detection accuracy and reliability. Experimental results demonstrate that the proposed method performs well in anomaly detection tasks within industrial control systems, showing promising potential for real-world applications.