Fault Tree Failure Diagnosis Using Dynamic Impact Ranking
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Solution Overview
Problem
Complex machines and systems present challenges in diagnosing failure causes quickly and accurately, especially under time pressure, as reliability parameters change over time due to factors like data updates, material aging, and maintenance, making it difficult to identify potential failure points for timely corrective actions.
Innovation Solution
A computer-implemented method and system that uses a fault tree analysis, incorporating IoT sensors and cloud services, to calculate fault tree importance measures and failure impact factors, ranking basic events by their contribution to a top event and updating these rankings dynamically based on changing reliability parameters, thereby identifying the most significant contributor to system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the machine or system complexity increases, then the system functionality and capabilities improve, but the difficulty of diagnosing failure causes increases
Solution Approach 1:
The patent segments the complex system into a fault tree structure with hierarchical levels (top event, intermediate events, basic events). This segmentation allows the diagnosis process to break down complex failure analysis into manageable components, where each node represents a specific system element or failure mode, making it easier to identify and analyze failure causes in complex systems
Solution Approach 2:
The patent introduces a computer-implemented diagnostic system as an intermediary between the complex machine/system and the operator. This intermediary automatically performs fault tree analysis, calculates importance measures, and generates diagnostic recommendations, reducing the direct cognitive burden on operators while maintaining comprehensive analysis of complex system failures
2Ease of operation
If traditional failure analysis methods are used, then the diagnostic process is simple, but the speed and accuracy of failure diagnosis deteriorates under time pressure
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the fault tree structure, pre-defining the relationships between system components and failure modes, and pre-calculating importance measures before actual failures occur. When a failure happens, the system can quickly retrieve and analyze the pre-prepared fault tree information, significantly reducing diagnosis time while maintaining comprehensive analysis
Solution Approach 2:
The patent implements feedback mechanisms where the diagnostic system continuously monitors system parameters, compares actual performance against expected values, and automatically updates the fault tree analysis based on real-time data. This feedback loop enables rapid iteration and refinement of diagnostic conclusions, improving both speed and accuracy under time pressure
3Measurement precision
If reliability parameters are updated dynamically, then the accuracy of failure prediction improves, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on calculating importance measures for only the most critical basic events and minimal cut sets, rather than performing exhaustive analysis of all possible failure combinations. This selective approach maintains high prediction accuracy for the most significant failure modes while reducing overall computational complexity and resource requirements
Data Source
AI summary
A computer-implemented method for failure diagnosis using fault tree can include: receiving a fault tree comprising a node representing a top event, a plurality of nodes representing respective basic events, and one or more logic gates connecting the plurality of nodes representing the respective basic events to the node representing the top event; obtaining reliability parameters corresponding to the basic events; calculating fault tree importance measures corresponding to the basic events; calculating failure impact factors of the top event corresponding to the basic events, wherein the failure impact factors of the top event are products of the corresponding reliability parameters and the corresponding fault tree importance measures; ranking the basic events based on the failure impact factors of the top event; and identifying a most significant contributor to the top event, wherein the most significant contributor is a basic event having the highest failure cause probability of the top event.