Temporal Error Code Analysis for Root Cause Detection
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Solution Overview
Problem
Electronic systems face challenges in identifying operational issues due to high rates of false alarms and complex relationships between Built-in-Test results, making it difficult to determine the root cause of issues, which leads to increased maintenance costs and unscheduled maintenance.
Innovation Solution
A system and method for detecting temporal relationships associated with a selected root cause by analyzing error codes and temporal data, using a processor to identify combinations of event indicators and temporal relationships uniquely linked to the root cause, thereby distinguishing relevant from irrelevant test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Built-in-Test is used to diagnose operational issues, then diagnostic capability is improved, but false alarm rate increases
Solution Approach 1:
The patent segments the diagnostic process by separating error code analysis into distinct phases: collecting error codes from multiple Built-in-Test results, analyzing temporal relationships between error codes, and comparing against known root cause patterns. This segmentation allows the system to process test results systematically rather than treating all error codes equally, thereby reducing false alarms while maintaining diagnostic capability.
Solution Approach 2:
The patent applies preliminary action by pre-establishing a database of error code combinations and their temporal relationships associated with known root causes. Before diagnosing a new operational issue, the system has already prepared reference patterns of error sequences, enabling it to compare current test results against known patterns and distinguish true faults from false alarms more accurately.
2Reliability
If multiple Built-in-Test results are analyzed, then diagnostic coverage is improved, but complexity of identifying root cause increases
Solution Approach 1:
The patent adds a temporal dimension to error code analysis by examining the sequence and timing of when error codes occur, not just their presence. This transforms the analysis from a static set of error codes to a dynamic temporal pattern, enabling the system to handle multiple test results more effectively by identifying characteristic error sequences associated with specific root causes, thereby managing complexity through pattern recognition rather than exhaustive analysis.
Solution Approach 2:
The patent uses copying by creating a reference database that stores pre-analyzed error code patterns and their associated root causes from previous operational issues. When a new diagnostic challenge arises, the system copies and compares current error patterns against this reference library, eliminating the need to analyze all possible combinations from scratch and reducing the complexity of root cause identification while maintaining comprehensive diagnostic coverage.
3Measurement precision
If comprehensive error code analysis is performed, then accuracy of root cause identification is improved, but time required for diagnosis increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing the temporal relationships and patterns of error codes associated with various root causes in a reference database. This preparation work is done in advance, so when a diagnostic situation arises, the system can quickly compare current error patterns against the pre-analyzed reference patterns, achieving high accuracy without performing time-consuming comprehensive analysis from scratch.
Solution Approach 2:
The patent implements feedback by using the results of temporal relationship analysis to guide further diagnostic steps. The system analyzes error codes in sequence, and based on the temporal patterns detected, it can feedback to prioritize certain analysis paths or confidently identify root causes when characteristic patterns are found, thereby reducing the overall time required while maintaining high accuracy through adaptive analysis depth.
Data Source
AI summary
Methods and systems are provided for detecting temporal relationships that are uniquely associated with a selected root cause. The method comprises identifying error codes associated with a root cause, wherein each error code comprises a plurality of event indicators and temporal data describing when the event indicator was generated, analyzing each of the error codes to detect a combination of event indicators that is associated with error codes corresponding to the selected root cause and to a non-selected root cause, and detecting a temporal relationship involving the combination of event indicators, wherein the temporal relationship is uniquely associated with error codes corresponding to the selected root cause.