Multi-Dimensional Cause-Effect Matrix for Complex System Fault Diagnosis
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Solution Overview
Problem
Complex systems, such as data processing pipelines, are difficult to diagnose due to their intricate dependencies and interactions, leading to increased costs and efforts in detecting and remedying defects, especially as they grow in size and complexity.
Innovation Solution
A method involving the collection of runtime data to construct a multi-dimensional cause-and-effect relation matrix among subcomponents, filtering abnormal operations, and using a result feedback mechanism to dynamically adjust weights and identify the Most Likely Related Path (MLRP) for effective troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system size and complexity increase to meet business demands, then the system can handle more operations and components, but the difficulty and cost of detecting and remedying defects increases exponentially
Solution Approach 1:
The patent segments the complex system into multiple subcomponents and creates a cause-and-effect relation matrix that breaks down the system into manageable units. Each subcomponent's operations are individually analyzed and correlated, allowing defects to be isolated and identified without having to analyze the entire complex system at once.
Solution Approach 2:
The patent introduces a cause-and-effect relation matrix as an intermediary tool between the system components and the defect analysis process. This matrix serves as a mediator that captures and represents the relationships between subcomponents, making the complex interactions visible and analyzable without directly examining the entire system.
2Reliability
If traditional testing and debugging methods are used in complex systems, then the process follows standard development stages, but the cost and time for detecting defects increases exponentially as the process progresses
Solution Approach 1:
The patent performs preliminary action by collecting runtime data and constructing the cause-and-effect relation matrix during system operation, rather than waiting for traditional testing stages. This allows defect patterns to be identified early through continuous monitoring and analysis of operational data, preventing defects from propagating through later development stages.
Solution Approach 2:
The patent implements a feedback mechanism where runtime data from system operations is continuously collected, analyzed through the cause-and-effect matrix, and used to identify defects. This feedback loop enables real-time or near-real-time defect detection, allowing the system to self-diagnose issues without waiting for scheduled testing phases.
3Measurement precision
If the scope of investigation is expanded to cover all subcomponents in a complex system, then more potential defects can be identified, but the time and resources required for troubleshooting increase
Solution Approach 1:
The patent adds a new dimension to the analysis by creating a multi-dimensional cause-and-effect relation matrix that goes beyond simple linear component sequences. This matrix captures relationships across multiple dimensions including causal relationships, temporal sequences, and interaction patterns, enabling precise defect localization without exhaustively investigating all components.
Solution Approach 2:
The patent changes the parameter of analysis from individual component inspection to relationship-based analysis. By focusing on the cause-and-effect parameters between subcomponents rather than examining each component in isolation, the system can quickly identify which relationships are abnormal and trace defects to their sources without expanding the investigation scope to all components.
Data Source
AI summary
A system and related method identify a weakness of a workflow in a complex system. The method collects runtime data about the complex system. The complex system comprises a plurality of subcomponents, and the method identifies an abnormal operation in the complex system. The method constructs a multi-dimensional cause-and-effect relation matrix among the plurality of subcomponents, and filters one or more related operations using the multi-dimensional cause-and-effect relation matrix.