Automated Defect Frame Selection for Asset Inspection
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Solution Overview
Problem
Current inspection methods for asset health, such as borescope-based inspections, are inefficient and error-prone due to subjective reliance on skilled operators to select relevant frames from numerous images, leading to inconsistent and inaccurate assessments.
Innovation Solution
A system and method utilizing image processing techniques like auto-distress ranking, structural similarity, and Hessian norm computation to automatically identify and select key frames representative of defects in assets, reducing subjectivity and enhancing inspection efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame selection by skilled operators is used, then inspection accuracy can be maintained through expert judgment, but inspection efficiency deteriorates due to time-consuming trial and error selection
Solution Approach 1:
The patent replaces the manual mechanical process of frame selection by operators with an automated image processing system. The system uses computational algorithms including gradient calculation, variance analysis, and distress ranking to automatically identify and select frames containing defects, eliminating the need for manual trial and error while maintaining inspection accuracy.
Solution Approach 2:
The inspection system performs self-service by automatically selecting relevant frames without human intervention. The algorithm independently analyzes captured images, calculates distress metrics, ranks frames by defect significance, and outputs selected frames for assessment, making the system self-sufficient in the frame selection task.
2Reliability
If numerous images are captured during inspection, then completeness of defect detection is improved, but time consumption worsens due to the large number of frames to review
Solution Approach 1:
The system extracts only the most relevant frames from the large set of captured images. By calculating distress metrics and ranking frames, the algorithm extracts and outputs only those frames that contain significant defect information, discarding or deprioritizing frames without defects. This extraction process maintains defect detection completeness while dramatically reducing the number of frames requiring human review.
3Productivity
If automated image processing techniques are used, then inspection efficiency is improved through automatic frame selection, but measurement precision may deteriorate without expert judgment
Solution Approach 1:
The system changes the parameters used for frame selection from subjective expert judgment criteria to objective quantitative parameters. It calculates numerical metrics including image gradients, variance, distress rankings, and defect probabilities, using these measurable parameters to objectively identify frames with defects. This parameter transformation enables automated systems to achieve accuracy previously requiring human expertise.
Data Source
AI summary
There are provided methods and systems for assessing the health of an asset. For example, a system is provided. The system may include a processor and a memory including instructions that, when executed by the processor, cause the processor to perform operations consistent with identifying a defect in a component of an asset. The operations may include fetching from an inspection system, a plurality of images acquired from an inspection of the component of the asset by the inspection system. The operations may include identifying, based on an image processing technique codified and included as part of the instructions, a subset of images from the plurality of images. The subset of images is representative of the defect in the component of the asset, and the image processing technique is selected from the group consisting of an auto-distress ranking technique, a structural similarity technique, a mean-subtracted filtering technique, and a Hessian norm computation technique.