In recent years, acoustic imaging technology for GIS defect detection has developed rapidly. This technology enables intuitive sound source localization, helping operation and maintenance personnel focus on the exact location of GIS defects, thereby improving the efficiency of defect analysis and resolution.
Sound source localization is only the first step. It would be even more ideal if common GIS defect types could be automatically identified using artificial intelligence (AI), along with intelligent recommendations for maintenance strategies.

Defects such as loose bolts, bellows movement, and loose internal shielding components causing abnormal noises can currently be located using acoustic imaging technology.
However, due to structural differences among GIS units from various manufacturers and models, as well as varying bay configurations across engineering projects, the generated sound signals often carry inherent characteristics. This adds complexity and technical challenges to GIS defect diagnosis using acoustic imaging.

Further progress requires close collaboration between acoustic specialists and power grid switchgear experts, integrating domain knowledge and field experience to iteratively test, refine, and optimize engineering-based acoustic and vibration signal analysis methods and algorithms.

With continuous advancements in acoustic imaging technology and improvements in AI algorithm efficiency, the application of more advanced technologies will significantly reduce the workload of on-site operation and maintenance personnel.