Cluster Anomaly Detection via Inferential Variable Derivatives
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for monitoring the health of components in mission-critical systems are inefficient, as they require numerous sensors and costly downtime for stress-testing, and fail to detect anomalies in real-time, leading to potential premature failures.
Innovation Solution
A system that monitors derivatives of inferential variables from sensors to detect anomalies in clusters of components, allowing for proactive remedial actions and reducing the need for multiple sensors by using a moving-window numerical derivative technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to monitor each component in a cluster, then measurement precision and anomaly detection capability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent combines multiple sensor readings from different components into a single aggregate metric that represents the overall health of the component cluster. Instead of monitoring each component individually with separate sensors, the system merges the data to create a collective health indicator, thereby reducing the number of sensors needed while maintaining effective anomaly detection capability.
Solution Approach 2:
The patent creates a universal monitoring approach where a single sensor per component monitors multiple aspects of component health simultaneously. The sensor data is used to derive multiple inferential variables that collectively assess component condition, eliminating the need for specialized sensors for each specific anomaly type.
2Reliability
If components are periodically removed from stress-test chambers for external testing, then reliability data is obtained, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent enables components to self-monitor their own health status through embedded sensors that continuously collect operational data. The components generate their own health metrics and anomaly detections without requiring external intervention or removal from the stress-test chamber, thereby maintaining continuous stress-testing productivity while obtaining reliable failure data.
Solution Approach 2:
The patent implements continuous monitoring of component health during stress-testing operations. Instead of periodically interrupting the stress-test chamber operations to remove components for external testing, the system maintains continuous data collection and analysis, ensuring uninterrupted stress-testing productivity while capturing complete reliability information.
3Reliability
If components are monitored continuously during operation, then reliability and early failure detection are improved, but use of energy and computing resources increase
Solution Approach 1:
The patent applies partial monitoring by focusing computational resources on detecting specific anomaly patterns rather than analyzing all sensor data in real-time. The system uses derivative calculations to identify only significant changes in component behavior, processing only the necessary portion of data to detect failures while minimizing overall computational energy consumption.
4Productivity
If fail-over mechanisms are used to minimize downtime, then system availability is improved, but loss of time occurs before fail-over can activate
Solution Approach 1:
The patent performs preliminary detection of component degradation and anomalies before actual failure occurs. By continuously monitoring component health metrics and detecting early signs of failure, the system can trigger preventive maintenance or fail-over actions before the component completely fails, thereby eliminating the downtime that would otherwise occur between failure and fail-over activation.
Data Source
AI summary
A system that detects multiple anomalies in a cluster of components is presented. During operation, the system monitors derivatives obtained from one or more inferential variables which are received from sensors in the cluster of components. The system then determines whether one or more components within the cluster have experienced an anomalous event based on the monitored derivatives. If so, the system performs one or more remedial actions.