PDF-Based Synthetic Data Generation for NDE Anomaly Detection
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Solution Overview
Problem
Existing NDE/NDT techniques face inefficiencies due to a lack of sufficient training data for anomaly detection, leading to inefficient implementation and reduced accuracy.
Innovation Solution
A method and system that generate simulated data using probability density functions (PDF) to extrapolate or interpolate experimental data, creating a large volume of training data for deep learning and machine learning models to detect anomalies, including new anomalies not present in the original data, and validate the models before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature-based classification, artificial neural networks and adaptive filtering techniques are applied for automatic radiographic inspections, then anomaly detection capability is improved, but implementation efficiency deteriorates due to lack of sufficient training data
Solution Approach 1:
The patent creates synthetic copies of experimental data by applying probability density functions to generate simulated data. This copying approach generates additional training data that mimics real experimental data characteristics, enabling efficient training of machine learning models without requiring extensive physical experimentation.
Solution Approach 2:
The patent performs preliminary data generation and model training by creating simulated training datasets before actual anomaly detection is needed. The simulated data is generated in advance using PDF-based methods, allowing the learning model to be pre-trained and validated, thus improving implementation efficiency when deployed for actual inspections.
2Measurement precision
If more experimental data is collected to train the learning model, then model accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
Instead of collecting additional physical experimental data, the patent creates synthetic copies through simulation. The PDF-based generation method reproduces the statistical characteristics of experimental data, providing sufficient training data without the time and resource costs of physical data collection.
Solution Approach 2:
The patent transforms the data generation approach by changing from physical experimentation to mathematical simulation. By using probability density functions and varying simulation parameters, the system generates diverse training data efficiently, avoiding the time-consuming nature of physical data collection while maintaining model accuracy.
Data Source
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AI summary
Method and system for detecting one or more anomalies in an object are provided. The system receives experimental data of the object and applies a probability density function (PDF) upon one or more variables associated with the experimental data to determine corresponding one or more PDF estimates. The system further generates simulated data associated with the object based on at least one of the one or more PDF estimates and priori data associated with the testing of the object. The simulated data comprises one or more new anomalies unknown in the experimental data along with the one or more anomalies of the experimental data. Furthermore, the system trains a learning model based on the one or more new anomalies and the one or more anomalies of the experimental data. The learning model is applied for detecting any anomaly in an object.