A review of research on intelligent fault detection of power equipment based on infrared and voiceprint: methods, applications and challenges
Xizhou Du, Xing Lei, Ting Ye, Yingzhou Sun, Zewen Shang, Zhiqiang Liu, Tianyi Xu · Global Energy Interconnection · 2025
As modern power systems grow in complexity, accurate and efficient fault detection has become increasingly important. While many existing reviews focus on a single modality, this paper presents a comprehensive survey from a dual-modality perspective-infrared imaging and voiceprint analysis-two complementary, non-contact techniques that capture different fault characteristics. Infrared imaging excels at detecting thermal anomalies, while voiceprint signals provide insight into mechanical vibrations and internal discharge phenomena. We review both traditional signal processing and deep learning-based approaches for each modality, categorized by key processing stages such as feature extraction and classification. The paper highlights how these modalities address distinct fault types and how they may be fused to improve robustness and accuracy. Representative datasets are summarized, and practical challenges such as noise interference, limited fault samples, and deployment constraints are discussed. By offering a cross-modal, comparative analysis, this work aims to bridge fragmented research and guide future development in intelligent fault detection systems. The review concludes with research trends including multimodal fusion, lightweight models, and self-supervised learning.