Methods for indication classification in thermal acoustic imaging inspection
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Solution Overview
Problem
Manual analysis of thermal acoustic imaging data for defect detection in components is time-consuming and prone to errors, especially in identifying anomalies like cracks in engine fan blades.
Innovation Solution
A method involving generating a training thermal acoustic imaging scan, extracting spatial and temporal features using a visual feature extractor model, and training a neural network model to predict defects or non-defects based on multiple sequence data from the scan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of thermal acoustic imaging data is performed, then defect detection can be conducted, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated neural network system. The neural network model processes thermal acoustic imaging data automatically, extracting spatial and temporal features to classify defects without human intervention, thereby eliminating time consumption and human error while maintaining detection accuracy
Solution Approach 2:
The system performs self-service through automated defect detection. The neural network model independently analyzes thermal acoustic imaging data, extracts features, and classifies defects without requiring manual input or intervention, enabling the system to serve itself in the defect detection process
2Productivity
If automated defect detection is implemented, then analysis time is reduced, but the complexity of the system increases
Solution Approach 1:
The patent segments the defect detection process into distinct functional components: data collection module, spatial feature extraction module, temporal feature extraction module, and classification module. This segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable and easier to implement while maintaining high productivity
Solution Approach 2:
The patent introduces an intermediary neural network model that mediates between the raw thermal acoustic imaging data and the final defect classification. This intermediary layer processes and transforms the data through feature extraction and temporal analysis, simplifying the overall system architecture while enabling automated high-speed detection
Data Source
AI summary
Methods are used by a thermal acoustic imaging (TAI) inspection system to identify potential defects within a component scanned, such as an engine fan blade. The inspection system generates a TAI scan having a plurality of frames. Indications are identified in at least one frame of the plurality of frames. An extractor model is applied to the plurality of frames to extract a plurality of spatial features corresponding to the indication. The plurality of spatial features is concatenated from each frame to generate a time series value of the values for the features. The plurality of spatial features is combined into multiple sequence data. The multiple sequence data includes a plurality of temporal features. The multiple sequence data is provided to train a neural network model to predict defects. For trained neural network models, the multiple sequence data is used to predict if the indication is a defect.