AI Flange Integrity Inspection for Leak-Causing Defect Detection
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Solution Overview
Problem
Traditional flange integrity inspections are time-consuming, require trained operators, and are limited to detecting loosened bolts or using ultrasonic testing, failing to identify various conditions that can cause leaks, and are inefficient for inexperienced operators.
Innovation Solution
An AI-based system using an industrial tablet with a mobile application captures images of flanges from multiple angles, employing a trained machine learning model to detect a variety of conditions, including defects like misalignment and corrosion, without the need for specialized training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional inspection methods are used, then detection accuracy for specific defects may be adequate, but inspection time and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated machine learning-based image analysis system. The system captures images of flanges and uses trained ML models to automatically detect defects, substituting the need for trained operators to perform visual inspections manually. This reduces inspection time while maintaining comprehensive defect detection capabilities.
Solution Approach 2:
The inspection system performs self-service by automatically analyzing flange images without requiring human intervention for defect identification. The machine learning model independently processes images, identifies defects, and generates inspection results, eliminating the need for operators to manually examine each flange component.
2Reliability
If traditional inspection methods are used, then trained operators can detect known defects, but the ability to identify various leak-causing conditions is limited
Solution Approach 1:
The machine learning model is designed with multi-functionality to detect multiple types of flange defects and conditions simultaneously. Rather than requiring separate inspection methods for different defect types, the single ML-based system can identify misalignment, corrosion, missing bolts, and other conditions that may cause leaks, providing comprehensive defect detection capability.
Solution Approach 2:
The system changes the detection parameters by using image analysis and machine learning algorithms to identify a broader range of defect characteristics. The ML model can detect subtle variations in flange appearance, geometry, and condition that traditional methods may miss, enabling identification of various leak-causing conditions beyond just loosened bolts.
3Measurement precision
If traditional inspection methods are used, then experienced operators can assess flange health, but inexperienced operators struggle to evaluate conditions properly
Solution Approach 1:
The patent replaces the human operator's expertise requirement with an automated machine learning system. The ML model consistently applies defect detection algorithms regardless of the operator's experience level, eliminating the variability in assessment accuracy between experienced and inexperienced operators. Any operator can use the system without specialized training in flange inspection.
Solution Approach 2:
The system performs the expert assessment function automatically without relying on human expertise. The machine learning model independently evaluates flange conditions and provides consistent, accurate measurements of defect severity and type, removing the dependency on operator experience for precise assessment.
Data Source
AI summary
A computer-implemented method for flange integrity classification using artificial intelligence is described. The method includes obtaining images of a flange, wherein an image of the images is captured at a predetermined angle of image capture. The method includes classifying a condition of the flange using a trained machine learning model. Further, the method includes rendering an indication of the condition of the flange.