Machine learning-based methods and systems for deffect detection and analysis using ultrasound scans

An inspection and assessment system for analyzing a sequence of ultrasound scan images of an asset and diagnosing a health condition of a section of the asset, the system comprising:

• an input-output interface arranged to receive an ultrasound scan image of the section of the asset;
• a feature extraction unit arranged to extract features of an aberration from the ultrasound scan image;
• a classification unit arranged to classify the aberration based on the extracted features;
• an aberration predictor unit arranged to analyze the extracted features and classification of the aberration, detect each aberration in the section and determine an aberration type, an aberration dimension or an aberration location for each aberration in the section;
• a labeler unit arranged to generate a diagnosis of a degree of health of the section and label the section with a section condition label;
• and
• an image rendering unit arranged to send an image rendering signal to cause a display device to render the section condition label on the display device with the ultrasound scan image.

A non-transitory computer readable storage medium containing aberration analysis and assessment program instructions for analysis of a sequence of ultrasound scan images of an asset and diagnosis of a health condition of a section of the asset, the program instructions, when executed by a processor, causing the processor to perform an operation comprising:

• receiving, by a machine learning platform, an ultrasound scan image of the section of the asset;
• analyzing, by the machine learning platform, the ultrasound scan image to detect any aberrations in the section;
• generating, by the machine learning platform, an aberration label for each detected aberration in the section;
• labeling, by the machine learning platform, the section of the asset with a section condition label;
• and,
• rendering, by a display device, the section conditional label,
• wherein the section condition label is based on each detected aberration in the section, and
• wherein the section condition label includes at least one of an aberration area ratio, a total number of aberrations, and the aberration label for each detected aberration in the section of the asset.