Non-destructive Evaluation Geometry Determination
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Solution Overview
Problem
Current non-destructive measuring methods for metallic objects, such as pipelines, face challenges in accurately distinguishing between defects and geometric deviations caused by installations or modifications, leading to unreliable load limit determinations and inefficient evaluation processes.
Innovation Solution
A method is developed to create a defect-free object grid using reference data sets from non-destructive measurements, allowing for automated classification of anomaly-free and anomaly-afflicted areas, and iterative adjustments to simulate accurate object geometry, enabling precise determination of load limits and defect geometries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated evaluation of measurement data is implemented, then evaluation independence and speed are improved, but measurement results are affected by local geometry changes making automated treatment difficult
Solution Approach 1:
The measurement data is divided into multiple measurement sections corresponding to different object sections. Each section is evaluated independently to determine local geometry characteristics and defect properties. This segmentation allows automated evaluation to handle local geometry variations (welds, attachments) by treating them as separate entities rather than confounding factors, thereby maintaining both automation speed and evaluation accuracy.
Solution Approach 2:
A simulation model of the object geometry acts as an intermediary between the measurement data and the evaluation algorithm. The simulation model incorporates expected geometry features (welds, attachments, brackets) and generates simulated measurement data that accounts for these features. By comparing actual measurements with simulated data, the system can distinguish between geometry-induced variations and actual defects, enabling reliable automated evaluation.
2Measurement precision
If manual evaluation by trained personnel is used, then defect geometry can be assessed, but evaluation is dependent on the person and time-consuming
Solution Approach 1:
The system creates a digital simulation model that copies the essential geometric features of the physical object (welds, attachments, brackets, nominal geometry). This virtual model generates simulated measurement data that replicates what would be measured from the actual object. The simulation model serves as a reusable template that eliminates the need for manual interpretation while maintaining consistent evaluation criteria, thereby reducing evaluation time without sacrificing precision.
Solution Approach 2:
The evaluation system uses parameter-based characterization of defects and geometry features. By defining specific parameters for defect properties (size, shape, location) and geometry features (weld dimensions, attachment positions), the system transforms qualitative manual assessment into quantitative automated measurement. This parameterization enables consistent, repeatable evaluations that are independent of the evaluator while maintaining high precision through defined measurement criteria.
3Reliability
If measurement data is used to determine load limits, then safety can be assessed, but geometric deviations from installations cause unreliable determinations
Solution Approach 1:
The evaluation process segments the measurement data into distinct categories: geometry features (welds, attachments, brackets), defects, and nominal geometry. By separating geometry-related signal variations from defect-related variations, the system preserves the information needed to distinguish between intentional modifications and actual defects. This segmentation enables reliable load limit determination by considering only defect-related deviations while accounting for geometry features as expected variations.
Solution Approach 2:
The simulation model acts as an intermediary that explicitly models expected geometry features. By incorporating welds, attachments, and other installations into the simulation model, the system can predict the measurement signatures these features produce. Comparing actual measurements with simulated data allows the system to identify deviations that exceed expected geometry variations, thereby preserving the distinction between geometry features and defects for accurate load limit assessment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables automated, accurate evaluation of defect geometries and load limits, improving the reliability of pipeline inspections and allowing for safer operating pressures and reduced maintenance costs by distinguishing between defects and geometric deviations.
Implementation Method 1
MFL examinations under consideration of the magnetic flux leakage density (MFL measuring methods) are preferably used for the detection of defects due to corrosion
Implementation Method 2
methods directly introducing ultrasound into the object wall, hereinafter referred to as ultrasonic methods or UT methods
Implementation Method 3
electromagnetic-acoustic methods (EMAT methods), in which sound waves, particularly in the form of guided waves, are generated in the pipe wall of the object to be examined due to eddy current-induced magnetic fields
Implementation Method 4
electromagnetic-acoustic methods (EMAT methods), in which sound waves, particularly in the form of guided waves, are generated in the pipe wall of the object to be examined due to eddy current-induced magnetic fields
Data Source
AI summary
A method is provided for determining the geometry of a metallic object, with in particular one or more real, examined defects, with a reference data set of the object generated on the basis of at least one measurement by at least one non-destructive measuring method, preferably comprising an at least partial representation of the object on or by an at least three-dimensional object grid by means of a computer unit. A classification of anomaly-free areas and anomaly-affected areas of the object is performed on the basis of at least parts of the at least one reference data set. An initial object grid is created, a prediction data set of the at least one non-destructive measurement method is calculated by a simulation routine using the initial object grid, at least parts of the prediction data set are compared with at least parts of the at least one reference data set, excluding the anomaly-afflicted regions, and the initial object grid is used as an object grid describing the geometry of the object as a function of at least one accuracy measure, or the initial object grid is iteratively adapted to the geometry of the object in the anomaly-free regions by means of the EDP unit.