Failure Analytics for Asset Management: Comparison of Drone vs. Traditional Inspections for Electrical Utilities

Utilities rely on a diverse mix of inspection methods to understand the condition of their assets, but determining which methods most effectively identify meaningful risks remains a central challenge for asset managers. In recent years, aerial inspections via drone have become widely used in utility space, having proven successful at providing perspectives not attainable through more traditional ground/climbing inspections while simultaneously keeping inspectors safely on the ground. For years, PG&E has utilized drone-based aerial inspections alongside traditional inspections for both its Transmission and Distribution electrical assets, but the relative effectiveness of these differing inspection types had remained not been quantified until recently. In this work we analyze the results of these large scale systematic studies conducted by PG&E, focusing not only the inspection findings between aerial vs. traditional inspection methods but also the inspection misses: a critical factor when utilities must prioritize limited maintenance resources. We present a statistical analysis on the survival rate of assets after inspection and quantitatively assess the effects of inspection finds/misses. This effort aligns with the broader wildfire risk and system reliability projects being conducted collaboratively by PG&E and Exponent, as missed conditions have the potential for disproportionate consequences when combined with ignition drivers or extreme weather. Quantifying these gaps helps utilities refine inspection protocols, improve condition assessment coverage, and develop long term asset strategies using these new technologies that more accurately and effectively identify and reduce real risk system-wide.