This study aims to develop a novel potential fault detection technology for gas insulated switchgear (GIS), which focuses on analyzing operational status information from multiple dimensions. Our main goal is to optimize the operational efficiency of GIS through this technology, making maintenance plans more scientific and reasonable, thereby enhancing the security performance of the entire system. The fault detection system created integrates monitoring data from multiple fields such as electrical, mechanical, and chemical, and uses cutting-edge feature extraction methods and machine learning algorithms to accurately capture various potential faults and their evolution trends. To verify the actual effectiveness of the system, this study carefully planned a series of rigorous testing processes, including fault feature offset analysis, recognition accuracy comparison testing, false alarm rate evaluation, and detection speed considerations. After a series of rigorous tests and verification, we found that the algorithm showed excellent recognition ability, and its monitoring accuracy almost reached an amazing level of 95%, and it still maintained a high degree of stability in a long-term continuous working environment. Compared with those traditional core algorithms, this new algorithm has made remarkable progress in reducing the frequency of false alarms and improving the speed and efficiency of fault identification. It is worth mentioning that this technology has opened up a brand-new path for predicting GIS (internal potential faults) by skillfully integrating multi-dimensional state monitoring indicators. This innovation has shown extremely attractive application potential and broad development prospects not only in the vast world of academic research, but also in many practical application scenarios.