Anomaly Detection via Image-Based Modeling
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Solution Overview
Problem
Traditional methods for detecting anomalies in systems, such as medical imaging systems, are costly, time-intensive, and require significant expertise due to the complexity of data preparation and analysis, often resulting in lengthy downtime and ineffective troubleshooting.
Innovation Solution
The method involves converting non-image data into image data using an image generator, creating training images for normal and non-normal events, and employing an image recognition model based on machine learning to analyze new data and identify anomalies by distinguishing between expected and non-normal events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data preparation and analysis methods are used for anomaly detection, then measurement precision may be maintained, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent replaces traditional mechanical data preparation processes with an image generation system that automatically converts system data into image representations. This substitution eliminates manual data preprocessing steps while maintaining anomaly detection accuracy through AI-based image analysis.
Solution Approach 2:
The patent introduces an intermediary image generation component that transforms raw system data into visual image representations. This intermediary layer simplifies the analysis process by converting complex data structures into visually interpretable formats that can be efficiently processed by image recognition models.
2Measurement precision
If traditional anomaly detection methods are used, then measurement precision may be maintained, but loss of time increases due to lengthy troubleshooting
Solution Approach 1:
The patent performs preliminary action by pre-training the image recognition model with extensive training data representing various system states and anomalies. This preliminary training enables the model to rapidly classify new system images without requiring time-consuming analysis during actual troubleshooting operations.
Solution Approach 2:
The patent substitutes time-intensive manual troubleshooting processes with automated image-based analysis. The system captures current system data, converts it to images, and uses the trained model to instantly identify anomalies, replacing lengthy traditional diagnostic procedures with rapid automated classification.
3Measurement precision
If traditional expert-based analysis is used, then measurement precision may be maintained, but ease of operation deteriorates due to expertise requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform anomaly detection without requiring expert human operators. The image recognition model independently analyzes system images and identifies anomalies, making the operation simple and accessible to users regardless of their expertise level while maintaining high detection accuracy.
4Measurement precision
If conventional data analysis approaches are used, then measurement precision may be maintained, but productivity decreases due to extensive data preparation
Solution Approach 1:
The patent replaces conventional mechanical data preparation and analysis systems with an automated image generation and recognition pipeline. This substitution dramatically improves productivity by eliminating manual data processing steps while maintaining measurement precision through the automated conversion of system data to images and subsequent AI-based analysis.
Data Source
AI summary
A method for detecting anomalies in a system. The method includes collecting training data from the system, converting the training data into training images using an image generator, and designating each of the training images as corresponding to events for the system, where the events are at least one of an expected normal event and a non-normal event. The method further includes generating an image recognition model based on the training images and the designations thereof. The method further includes collecting new data from the system, converting the new data into input images, and analyzing the input images using the image recognition model to determine which of the events for the system are represented in the input images, where the anomalies are detected when the input images are determined to at least one of represent a non-normal event and fail to represent an expected normal event.