Failure Signature Library for Computer System Telemetry Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fault monitoring systems in computer servers can detect anomalies but lack the ability to automatically identify the root cause of faults, requiring human intervention and real-time monitoring which is impractical.
Innovation Solution
A system that constructs a library of failure signatures from known faulty components, using auto-associative memory matrices to match and identify failure mechanisms in telemetry signals, even with low-resolution or low-sampling-rate data, by reconstructing high-resolution signatures from incomplete signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human engineers manually monitor telemetry signatures to identify failure mechanisms, then diagnostic accuracy is improved, but labor cost and scalability deteriorate
Solution Approach 1:
The system creates a library of copied failure signatures from historically faulty components. Each signature captures the characteristic telemetry pattern of a specific failure mechanism. When monitoring live components, the system compares current telemetry against this library of copied signatures to automatically identify failures, eliminating the need for human engineers to manually analyze each case while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service by automatically performing the diagnostic function that previously required human engineers. The telemetry monitoring system compares signals against the failure signature library and autonomously identifies failure mechanisms, allowing the system to serve its own monitoring and diagnostic needs without external human intervention for each fault case.
2Measurement precision
If high-resolution telemetry signals are collected to accurately identify failure mechanisms, then diagnostic accuracy is improved, but data processing complexity and storage requirements deteriorate
Solution Approach 1:
The system extracts only the essential failure signature patterns from high-resolution telemetry data during the learning phase. Instead of storing and processing all raw high-resolution data, it extracts the characteristic patterns that define each failure mechanism. This extracted signature library can then be used to identify failures in new components without needing to process the full high-resolution data streams, reducing ongoing computational complexity.
3Loss of time
If continuous human monitoring is implemented to detect failure mechanisms in real-time, then fault detection timeliness is improved, but operational cost deteriorates
Solution Approach 1:
The system replaces the mechanical system of human monitoring with an automated computational system. The telemetry comparison algorithm automatically compares live signals against the failure signature library in real-time, providing timely fault detection without requiring human engineers to continuously monitor systems. This substitution eliminates labor costs while maintaining or improving detection timeliness through automated real-time comparison.
Data Source
AI summary
One embodiment of the present invention provides a system that identifies a failure mechanism for a component. The system first constructs a library of failure signatures from a set of components, wherein each failure signature in the library characterizes a known failure mechanism associated with a component in the set of components. Next, the system receives a telemetry signal which is measured from a component under surveillance. The system then determines if there is an anomaly in the telemetry signal. If so, the system performs pattern-matching operations between the telemetry signal and failure signatures in the library. Next, if a match is found, the system identifies the failure mechanism for the component under surveillance based on the matching failure signature.