Self-Learning Integrity Management for Anomaly Prediction
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Solution Overview
Problem
Existing tools for analyzing complex systems are unable to accurately predict future events due to the need for precise programming and understanding of all variables, leading to inefficient thresholding systems that produce false-positive alerts and fail to address underlying abnormalities in a timely manner.
Innovation Solution
An integrity management system that uses a self-learning dynamic thresholding module to monitor and predict abnormalities by generating heuristics based on historical data, capturing fingerprints of nodes, and applying them to real-time data to alert potential future events before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing thresholding systems are used to detect abnormalities, then potential problems can be identified, but false-positive alerts increase and system efficiency decreases
Solution Approach 1:
The patent implements dynamic thresholds that automatically adapt to changing system conditions and historical data patterns, replacing static threshold values. This allows the system to distinguish between normal fluctuations and actual abnormalities, reducing false-positive alerts while maintaining reliable detection capability.
Solution Approach 2:
The system employs self-learning algorithms that automatically analyze historical data, identify patterns, and refine threshold settings without manual intervention. This self-service capability enables the system to continuously improve its abnormality detection accuracy while reducing the operational burden on personnel.
2Reliability
If static thresholding is applied to monitor system variables, then abnormal conditions can be detected, but the system cannot adapt to changing operational patterns
Solution Approach 1:
The patent transforms static thresholds into dynamic, adaptive values that automatically adjust based on historical system behavior and changing operational conditions. This enables the monitoring system to remain reliable across varying workloads and environmental conditions without requiring manual threshold reconfiguration.
Solution Approach 2:
The system incorporates feedback loops where detected abnormalities and system responses are fed back into the learning algorithm, which then refines future threshold settings. This continuous feedback mechanism enables the system to adapt to new patterns and improve detection accuracy over time.
3Loss of time
If sensitive thresholding is used to detect early abnormalities, then potential issues can be identified sooner, but false-positive alerts increase significantly
Solution Approach 1:
The patent implements dynamic thresholds that adapt their sensitivity based on learned system patterns, enabling early detection of true abnormalities while filtering out normal variations. This resolves the trade-off by making the system simultaneously sensitive to real issues and selective against false alarms.
Solution Approach 2:
The system performs preliminary analysis of system behavior patterns and establishes baseline expectations before actual monitoring begins. This preliminary action enables the system to recognize deviations from normal operation more accurately, reducing false positives while maintaining early detection capability.
Data Source
AI summary
An integrity management system predicts abnormalities in complex systems before they occur based upon the prior history of abnormalities within the complex system. A topology of the nodes of a complex system is generated and data is collected from the system based on predetermined metrics. In combination with dynamic thresholding, fingerprints of the relevant nodes within a complex system at various time intervals prior to the occurrence of the abnormality are captured and weighted. The fingerprints can then be applied to real-time data to provide alerts of potential abnormality prior to their actual occurrence.