Random-Projection Anomaly Detection for High-Sensor MSET Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional anomaly detection methods, such as MSET-SPRT, face performance and accuracy issues when dealing with large numbers of sensors due to high-dimensional models and excessive hypothesis testing, leading to inefficient processing and increased false positives or false negatives.
Innovation Solution
Implementing a method that uses random projections to reduce dimensionality and consolidate statistical analysis into global or regional aggregates, combined with Monte Carlo simulation to improve performance and reduce type 1 and type 2 errors, thereby transforming hypothesis testing into a binomial test for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional anomaly detection methods (MSET-SPRT) are applied to large numbers of sensors, then comprehensive monitoring coverage is achieved, but processing efficiency deteriorates and false positives/negatives increase
Solution Approach 1:
The patent segments the high-dimensional sensor data into multiple lower-dimensional subspaces using random projection. Instead of performing anomaly detection on all sensors simultaneously, the system divides the sensor set into groups and applies separate hypothesis tests to each group, reducing the computational burden while maintaining detection accuracy.
Solution Approach 2:
The patent combines multiple hypothesis tests across different sensor groups using a binomial test framework. By merging the results of individual hypothesis tests through statistical aggregation, the system achieves comprehensive anomaly detection across all sensors while avoiding the inefficiency of testing each sensor independently with high dimensionality.
2Productivity
If dimensionality is reduced using random projections, then processing efficiency improves, but measurement precision may deteriorate
Solution Approach 1:
The patent changes the parameter space by transforming high-dimensional sensor readings into lower-dimensional random projections. This parameter transformation reduces computational complexity while preserving the statistical properties needed for anomaly detection, as the random projections maintain the essential variance and covariance structures of the original data.
Solution Approach 2:
The patent substitutes the conventional MSET-SPRT mechanical processing system with a statistical approach based on random projections and binomial tests. This substitution replaces complex multivariate state estimation with simpler statistical hypothesis testing, improving efficiency while maintaining precision through proper statistical aggregation.
3Productivity
If the number of hypothesis tests is reduced, then processing efficiency improves, but detection coverage may worsen
Solution Approach 1:
The patent introduces a new dimensional framework by organizing hypothesis tests across multiple sensor groups and aggregating results through a binomial test. This dimensional reorganization allows the system to maintain comprehensive detection coverage by testing multiple groups simultaneously rather than reducing the number of tests in a single dimension.
Solution Approach 2:
The patent introduces the binomial test as an intermediary mechanism that aggregates results from multiple hypothesis tests. This intermediary statistical framework combines the outcomes of individual sensor group tests, ensuring that detection coverage is maintained across all sensors while reducing the overall computational burden through efficient statistical aggregation.
Data Source
AI summary
Disclosed is an approach to implement improved anomaly detection. Improved anomaly detection is provided using MSET-SPRT via Monte Carlo simulation that can address problems with conventional MSET-SPRT approaches and provide improved system performance and accuracy.