Utility Asset Image Tagging for Accurate Condition Assessment
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Solution Overview
Problem
Conventional manual inspection and rule-based image processing systems for utility assets are time-consuming, prone to errors, and lack scalability and flexibility in identifying nuanced asset conditions.
Innovation Solution
An asset condition generator utilizing a transformer-based neural network ML model analyzes multiple images of utility assets captured at different angles to generate descriptive tags characterizing their condition, state, and type, integrating reinforcement learning for improved accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used to monitor utility assets, then operational reliability can be maintained through thorough examination, but time consumption and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system using machine learning models. The system captures images of utility assets and uses ML algorithms to automatically analyze conditions, identifying issues such as corrosion, damage, or operational status without human intervention in the field.
Solution Approach 2:
The system enables utility assets to be self-assessed through automated image analysis. The ML model independently evaluates asset conditions from captured images, generating condition assessments and maintenance priorities without requiring human inspectors to physically examine each asset.
2Productivity
If rule-based image processing systems are used to analyze utility asset images, then processing speed improves, but accuracy in identifying nuanced asset conditions deteriorates
Solution Approach 1:
The patent transitions from fixed rule-based processing parameters to adaptive machine learning parameters. The ML model learns optimal detection parameters from training data and can adjust its analysis approach based on the specific characteristics of each utility asset image, improving accuracy for diverse asset types and conditions.
Solution Approach 2:
The system employs dynamic, adaptive image processing where the ML model adjusts its analysis based on learned patterns. Rather than applying static rules, the model dynamically identifies relevant features and conditions in each image, adapting to variations in asset types, environmental conditions, and damage patterns.
3Ease of manufacture
If conventional inspection systems are used for utility assets, then implementation simplicity is maintained, but scalability to handle large volumes of assets deteriorates
Solution Approach 1:
The patent creates a universal image processing platform that can handle multiple types of utility assets (power lines, transformers, poles, etc.) through a single ML model system. The model is trained to recognize various asset types and conditions, enabling the same system to scale across diverse asset inventories without requiring separate inspection systems for each asset class.
Data Source
AI summary
A condition generator analyzes a set of utility asset images of a particular utility asset using the ML (machine learning) model identify a type and condition of the particular utility asset depicted in the set of utility asset images. The identified condition is assigned a confidence score, and the set of utility asset images includes at least two images of the particular utility asset captured at different angles. The condition generator generates a descriptive tag for the set of utility asset images based on the identified type and condition. The descriptive tag characterizes an operational status of the particular utility asset. The condition generator stores the set of utility asset images and the generated descriptive tag in a utility asset database. The utility asset database stores images of utility assets.