Automated Health Indicator Analysis for Reactive Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex reactive systems experience prolonged downtime due to the overwhelming amount of operational logs, making it difficult for users to determine the relevance of data for system health, and the scarcity and high cost of expert diagnostics.
Innovation Solution
A diagnostic tool that automatically analyzes critical health indicators from signals, decomposing them into basic components to identify the root cause of faults, reducing downtime and the need for expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated diagnostic tools are implemented, then productivity is improved by enabling non-experts to diagnose system health, but measurement precision deteriorates compared to expert-level diagnosis
Solution Approach 1:
The patent creates a digital copy of expert diagnostic knowledge by training machine learning models on expert-annotated operational logs and health assessments. This allows the automated tool to replicate expert-level diagnostic reasoning without requiring actual experts to be present, thereby maintaining high accuracy while dramatically improving productivity and accessibility.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw operational logs and diagnostic conclusions. This intermediary layer processes multitudes of recorded data, identifies relevant features, and generates health assessments that bridge the gap between automated efficiency and expert-level precision, enabling non-experts to achieve accurate diagnoses.
2Measurement precision
If all operational log data is analyzed, then measurement precision is improved by capturing complete system information, but loss of time increases due to the sheer amount of data processing required
Solution Approach 1:
The patent extracts only the most relevant features and data points from multitudes of operational logs using feature selection techniques and domain knowledge. By taking out only the critical information needed for health assessment rather than processing all available data, the system maintains measurement precision while dramatically reducing the time required for analysis.
Solution Approach 2:
The patent segments the diagnostic process into distinct stages: data collection, feature extraction, pattern recognition, and health assessment. This segmentation allows the system to process data efficiently at each stage, focusing computational resources on the most informative aspects of operational logs rather than treating all data equally, thereby reducing overall processing time while maintaining accuracy.
Data Source
AI summary
A signal from a system, such as a reactive system, that reflects health indicators of the system may be selected. A signal analyzer may extract the health indicators from the signal and conduct a diagnostics of the health of the system based on the health indicators.