Vehicle-Mounted AI Pole Inspection for Grid Fault Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional manual inspection of utility poles is time-consuming and prone to errors, failing to effectively address the need for reliable power distribution amidst unpredictable weather events.
Innovation Solution
A vehicle-mounted, camera-based inspection system with AI-powered software that passively collects data using stereo cameras, inertial measurement units, and GPS for real-time utility pole detection, GPS location, situational awareness, and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used, then device complexity is low, but productivity is low and measurement precision is poor
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated vehicle-mounted inspection system equipped with stereo cameras, LiDAR, and AI processing. The system automatically captures images, processes them through deep learning models, and generates inspection reports without human intervention, thereby significantly improving productivity while accepting increased device complexity
Solution Approach 2:
The patent creates digital copies of utility poles and infrastructure components through photogrammetry and LiDAR scanning. These 3D models and point clouds serve as virtual representations that can be analyzed, measured, and monitored without physical contact, enabling efficient automated inspection of multiple poles simultaneously
2Measurement precision
If manual inspection is performed, then device complexity is low, but measurement precision and reliability are insufficient
Solution Approach 1:
The patent replaces human visual inspection with automated computer vision systems using stereo cameras and deep learning algorithms. The AI models automatically detect, measure, and classify infrastructure conditions with consistent precision, eliminating human error and providing reliable measurement data
Solution Approach 2:
The patent introduces AI algorithms and image processing software as intermediary layers between the camera sensors and the inspection results. These intermediaries process raw images through multiple processing stages including enhancement, feature detection, and condition assessment, achieving high measurement precision through computational analysis
3Loss of time
If traditional inspection methods are used, then loss of time is high, but productivity is low
Solution Approach 1:
The patent enables continuous inspection operation by mounting sensors on vehicles that can continuously travel through infrastructure areas. The system operates without interruption as the vehicle moves, capturing images and data continuously rather than requiring discrete manual inspection stops, thereby reducing total inspection time while maintaining high productivity
Solution Approach 2:
The patent performs preliminary data capture and processing during routine vehicle operations or scheduled passes, preparing inspection data in advance before failures occur. The system continuously monitors and pre-processes images, allowing for rapid analysis and immediate response when issues are detected, reducing overall time loss
Data Source
AI summary
Disclosed is an artificial intelligence-based platform for infrastructure inspection, localization and maintenance of power grid structures that advantageously allows electrical utility service providers to evaluate the condition of distribution grid components at scale before failures. Our inventive systems, and methods of the platform employ a vehicle-mounted, camera-based inspection system that passively collects maintenance-related data during routine operation. A vehicle mounted computing platform—coupled with multiple sensors including stereo cameras, inertial measurement unit, and global positioning system—are combined with AI powered software that provides multiple monitoring applications including utility pole detection, pole GPS location, situational awareness/object recognition of any pole-mounted instruments, and anomaly detection resulting from weather-related or other events.