Temporal Data Mining for Fault Root Cause Diagnostics
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Solution Overview
Problem
In manufacturing environments, diagnosing the root cause of machine and system faults is challenging due to the complexity of fault sequences over time, as traditional methods fail to accurately identify the underlying causes of faults that result from series of events.
Innovation Solution
A method and system for fault data correlation using temporal data mining, which involves receiving fault data, identifying episodes within the data, calculating frequency and correlation confidence, and outputting reports to assist in root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault diagnosis methods are used to examine immediate preconditions before a fault occurs, then the diagnostic process is simple and quick, but the accuracy of determining the root cause is insufficient because some faults are the result of a series of events
Solution Approach 1:
The patent segments the fault diagnosis process into distinct phases: data collection phase (gathering fault data and operational parameters), episode identification phase (detecting recurring fault patterns), and analysis phase (determining root causes based on temporal relationships). This segmentation allows the system to handle complex temporal sequences while maintaining organized and manageable diagnostic procedures, thereby improving root cause identification accuracy without overwhelming complexity
Solution Approach 2:
The system performs preliminary actions by collecting and storing fault data, operational parameters, and event sequences before actual fault diagnosis is needed. This pre-processing and archiving of temporal data enables the system to quickly retrieve and analyze historical fault patterns when faults occur, improving diagnostic accuracy while reducing the complexity of real-time analysis
2Measurement precision
If temporal data mining is applied to analyze sequences of faults and calculate correlation confidence, then the root cause identification accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies local quality by focusing the temporal data mining process on specific episodes and fault sequences that are most relevant to the current diagnostic need. Rather than analyzing all possible fault combinations, the system identifies and concentrates computational resources on detecting and analyzing recurring fault patterns (episodes) with significant correlation confidence, thereby achieving high accuracy while reducing overall processing time
Solution Approach 2:
The system performs partial action by calculating correlation confidence for only the most significant fault sequences and episodes identified during analysis. Rather than computing exhaustive correlations for all possible fault combinations, the system focuses computational effort on episodes that meet predetermined significance thresholds, achieving sufficient diagnostic accuracy with reduced processing time and computational resources
Data Source
AI summary
A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.