## 5 Conclusions and Outlook  
SF\({}_{6}\) leakage monitoring, decomposition product diagnosis, and detection technology are three important components for accurately and timely judging the insulation state of electrical equipment and ensuring the reliability and safety of the power system. Efficient, reliable, and fast monitoring and diagnostic technology can reduce the workload of staff and increase labor efficiency. In this work, the decomposition mechanism of SF\({}_{6}\) in the insulation equipment and the reasons affecting the type and concentration of products were summarized, and various detection techniques for SF\({}_{6}\) decomposition by-products were introduced to analyze the insulation condition. The trend analysis of sensitivity and emission concentration by carbon nanotube materials and two-dimensional nanomaterials for different decomposition products were summarized, which provided theoretical support for sensing materials related to online monitoring technology. The content of SF\({}_{6}\) and its decomposition products is closely related to the purity of SF\({}_{6}\) itself and the surrounding environmental conditions. It is of great significance to understand the reaction and monitor it for the maintenance and safe operation of the equipment. The existing detection methods, such as large-scale equipment inspection, are expensive and complex to operate. Although the cost of miniaturized equipment is low, its detection accuracy is low and can easily be affected by the environment; so, it is difficult to realize real-time monitoring. Gas-sensitive materials are the core components of monitoring equipment, and the integration of the equipment has become its primary problem such that it needs to be combined with other online monitoring devices, and the monitoring gas range is limited. Gas-sensitive materials can be combined with prior art in a variety of ways. Using gas-sensitive materials as samples or components, the spectral changes under different gases are analyzed. The gas-sensitive materials are integrated into the optical path, and the sensitive response of the gas-sensitive material to a specific gas is used to enhance the signal strength or selectivity of the infrared spectrum. Combined with the change in electrical properties of gas-sensitive materials and the spectral analysis of prior art, a model was built to improve the accuracy of detection. Due to the existence of various factors affecting the decomposition state of SF\({}_{6}\) and the interference between products, there is no established online monitoring method to detect multiple decomposition components simultaneously. It is a promising detection method for the analysis of SF\({}_{6}\) and its by-products using a combination of spectroscopy and chemical sensors by machine learning algorithms plus artificial synapses, which can detect multiple components, has strong anti-interference performance with high detection accuracy, and can be commercialized in the future. Artificial intelligence gas monitoring is an important direction of gas monitoring of power equipment for the future. Intelligent inspection can be realized by remote control, with high safety and high efficiency. Real-time data transmission can be carried out during inspection, and the monitoring range is wide. However, the improvement of its environmental resistance and adaptability (high temperature, high pressure, and toxicity), battery life, and data accuracy needs more attention. Writing--original draft preparation, L.Y. ; writing--review, S.W. ; supervision, C.C. ; validation, Q.Z. ; discussion, R.S. ; conceptualization, methodology, and funding, Y.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (No. 2023 YFC3707201), the National Natural Science Foundation of China (No. 52320105003), and the Informatization Plan of Chinese Academy of Sciences (Grant No. CAS-WX2023 PY-0103). Not applicable.