Sensor Data Anomaly Detection via Background Suppression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning techniques for anomalous state detection in sensor data, such as neural networks, require large amounts of training data and are computationally expensive, and often struggle to explain their detection results, leading to reduced confidence in accuracy.
Innovation Solution
A system that suppresses background sensor readings by calculating significance parameters and attenuation coefficients, detects anomalous objects using local peak analysis, and classifies them using a trained classifier, optimizing background suppression and classification performance with minimal training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used to detect anomalous states in sensor data, then detection accuracy may improve, but the amount of training data required increases and computational costs increase
Solution Approach 1:
The patent extracts and suppresses the background component from sensor data, separating it from the anomalous signals. By removing the dominant background portion, the system can detect anomalies with minimal training data, as the background suppression parameters are learned rather than requiring extensive labeled examples.
Solution Approach 2:
The system changes the parameter representation by learning background suppression parameters that transform the original sensor data into a background-suppressed form. This parameter transformation enables effective anomaly detection without requiring large amounts of training data, as the learned parameters capture the essential background characteristics.
2Measurement precision
If neural networks are used to detect anomalous states in sensor data, then detection accuracy may improve, but computational expense increases
Solution Approach 1:
By extracting and suppressing the background component, the system reduces the complexity of the detection task. The background suppression step simplifies the data structure, allowing for more efficient subsequent processing and classification with reduced computational expense.
Solution Approach 2:
The patent segments the sensor data processing into distinct stages: background suppression, anomaly detection, and classification. This segmentation allows each stage to be optimized independently, reducing overall computational expense while maintaining detection accuracy.
3Extent of automation
If neural networks are used to detect anomalous states, then automated detection is achieved, but the ability to explain detection results decreases
Solution Approach 1:
The background suppression process acts as an intermediary that produces a background-suppressed data representation. This intermediate form provides explanatory information about what constitutes the anomaly, as it explicitly shows what remains after removing the expected background, making the detection results more interpretable.
4Reliability
If background suppression is optimized for anomaly detection, then detection effectiveness improves, but classification performance may be affected
Solution Approach 1:
The system dynamically adjusts the background suppression level based on the specific application and data characteristics. By making the suppression level adjustable and adaptable, the system can optimize for detection effectiveness in one context while maintaining classification performance in another, resolving the trade-off between the two objectives.
Data Source
AI summary
A system is provided for background suppression and anomaly detection/classification in a sensor data field using an omnidirectional stochastic technique to expose anomalies. For each element in the sensor data field, the system identifies neighborhoods of elements that cover the various nearby parts of the sensor data field in all directions. At a specified statistical significance level for background, the system considers the element to be background if it is statistically insignificant relative to the elements in any one of the surrounding neighborhoods. The system exposes anomalous objects by applying an attenuation coefficient near zero to those background elements. The system grows anomalous objects from seed elements that correspond to local peaks in the background-suppressed sensor data field. The system can be trained to jointly learn an effective statistical significance level for background suppression and the parameters for classifying objects as of interest or not of interest.