Anomaly Detection in Integrated Parameter Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex integrated parameter systems face challenges in detecting anomalies across multiple nodes and vast data sets, as existing methods rely heavily on administrator judgment and are not scalable, prone to biases, and limited in adaptability to various industries and technologies.
Innovation Solution
A technology framework and algorithm utilizing real-time signal monitoring, anomaly detection, and alarm management, combined with multi-dimensional mutual information theory and locality-sensitive hashing, to classify interrelationships and correlations between data streams, allowing for objective anomaly detection and configuration by end users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing anomaly detection methods are used, then specific failures or attacks can be detected, but the system is not adaptable to unknown types of failures or attacks
Solution Approach 1:
The system performs self-learning by automatically analyzing historical data to build mathematical models of normal operation without requiring administrator intervention. The anomaly detection algorithm continuously refines its understanding of normal patterns, enabling it to detect previously unknown failure modes while maintaining high accuracy for known issues.
Solution Approach 2:
The system dynamically adjusts detection parameters by learning from historical data patterns. It modifies the mathematical models of normal operation based on accumulated knowledge, allowing the detection thresholds and criteria to adapt to changing system behaviors and emerging failure patterns without manual reconfiguration.
2Measurement precision
If administrator knowledge and expertise are used to identify anomalies, then specific anomalies can be detected, but biases and assumptions limit the system's objectivity
Solution Approach 1:
The system eliminates administrator bias by performing autonomous analysis of data patterns. The mathematical models are constructed automatically from historical data without human intervention, ensuring objective detection criteria that do not reflect individual administrator preferences or assumptions.
Solution Approach 2:
The patent replaces human judgment with automated computational algorithms. The anomaly detection process uses mathematical models and statistical analysis instead of administrator expertise, substituting mechanical computation for human decision-making to achieve consistent, unbiased results.
3Reliability
If multiple data streams from multiple nodes are monitored, then comprehensive system coverage is achieved, but the complexity of analyzing interrelationships increases
Solution Approach 1:
The system divides the complex data analysis task into manageable segments by analyzing individual data streams and their pairwise relationships separately. The mathematical models are built for each parameter combination independently, then integrated to provide comprehensive system monitoring without overwhelming computational complexity.
Solution Approach 2:
The patent transforms the complexity of multi-dimensional data relationships into a structured mathematical framework. By representing interrelationships as mathematical models with defined parameters, the system converts complex pattern recognition into systematic calculations that can be processed efficiently across multiple data streams.
4Measurement precision
If historical data is used to create mathematical models, then normal operation patterns are established, but the system cannot adapt to changing operational conditions
Solution Approach 1:
The system incorporates feedback mechanisms where detected anomalies and changing operational patterns are fed back into the mathematical models. This continuous feedback loop allows the models to be updated with new information, enabling the system to adapt to changing conditions while maintaining an accurate baseline of normal operation.
Solution Approach 2:
The mathematical models are designed to be dynamic rather than static, allowing them to evolve as new historical data is accumulated. The system automatically updates its understanding of normal operation patterns, transforming the models from fixed representations to adaptive frameworks that respond to changing system behaviors over time.
Data Source
AI summary
A system, method, and tangible computing apparatus is disclosed for the detection of anomalies in an integrated data network. Said system, method and apparatus comprises the creation and construction of a mathematical model that utilizes multi-dimensional mutual information to detect interactions and interrelationships between pairs of data streams and among pluralities of data streams. Real-time analysis of the operations of an integrated data network is enhanced and expedited via use of locality sensitive hashing that relies on density determinations of clusters of data.