Machine Learning Corrosion Prediction for Pipes Under Insulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current inspection techniques for predicting corrosion under insulation (CUI) in pipes are inaccurate and invasive, leading to inefficiencies in asset management and increased costs due to missed detections, false positives, and measurement variance.
Innovation Solution
A machine learning-based system that receives data on pipe operating conditions, applies a machine learning model to determine the likelihood of corrosion, and outputs the results automatically, prioritizing inspections for high-risk assets and deferring low-risk ones, using data from inspection history, asset data, production data, and environmental data to train models for corrosion initiation, short-term, and long-term pipe wall thickness predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current inspection techniques are used to predict corrosion under insulation, then inspection coverage is achieved, but measurement precision and reliability are poor due to inaccuracy and high variance
Solution Approach 1:
The patent replaces traditional mechanical inspection methods (physical inspection of insulated pipes) with a machine learning-based predictive system that uses environmental data, asset data, and inspection history to predict corrosion likelihood. This substitution eliminates the inaccuracy and subjectivity of manual inspections while providing consistent, data-driven predictions across all inspected assets.
Solution Approach 2:
The patent creates a virtual model of corrosion risk by training machine learning algorithms on historical inspection data and environmental factors. This digital twin approach allows the system to predict corrosion outcomes without physically inspecting each asset, thereby improving measurement precision while maintaining reliability through consistent application of the same predictive model across all assets.
2Productivity
If traditional inspection methods are applied to all assets, then comprehensive coverage is achieved, but loss of time and increased costs occur due to inspecting low-risk assets
Solution Approach 1:
The patent applies local quality by differentiating inspection priorities based on individual asset characteristics and predicted corrosion risk. Instead of uniform inspection schedules, the system tailors inspection frequency and intensity to each asset's specific risk profile, thereby improving overall inspection efficiency while reducing time spent on low-risk assets.
Solution Approach 2:
The patent performs preliminary risk assessment using machine learning models before scheduling physical inspections. By predicting corrosion likelihood in advance based on environmental data and asset characteristics, the system prepares an optimized inspection schedule that prioritizes high-risk assets, thereby improving productivity and reducing unnecessary time expenditure on low-risk assets.
3Extent of automation
If manual inspection processes are used, then inspection data is collected, but device complexity and operational difficulty increase due to user intervention requirements
Solution Approach 1:
The patent implements self-service by enabling the inspection system to automatically collect data, apply machine learning models, generate corrosion predictions, and output results without requiring manual user intervention at each step. The system autonomously processes inspection data and produces actionable insights, thereby improving automation extent while maintaining ease of operation through a streamlined workflow.
Data Source
AI summary
A machine learning based method for determining a likelihood of corrosion of a component is provided. The method comprises receiving data associated with a portion of at least one component, the data describing one or more operating conditions of the portion of the at least one component, applying, to the data associated with the portion, a first machine learning model, determining, responsive to the applying of the first machine learning model, a likelihood of corrosion specific to the at least one component based at least in part on the one or more operating conditions of the portion, and outputting, automatically and without user intervention, the likelihood of corrosion specific to the at least one component on a display.