Function-Based Failure Management for Complex System States
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Solution Overview
Problem
Current systems fail to effectively handle simultaneous failures in complex safety-critical systems, such as aircraft, due to the combinatorial explosion of possible failure scenarios, leading to overwhelming and illogical intervention procedures that are difficult for operators to manage, especially in non-deterministic processes.
Innovation Solution
An automated intervention method based on system intended functions rather than components, using an integration framework that organizes and modifies procedures according to context, selecting between different intervention processes through simulation models, and employing a System State Graph to guide interventions in complex scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional component-based procedures are used to handle system failures, then single system failures can be managed according to predefined protocols, but multiple simultaneous failures create combinatorial explosion of scenarios that become impossible to define procedures for each case
Solution Approach 1:
The patent segments the complex failure analysis problem into two independent parts: (1) identifying which systems have failed using the system state graph, and (2) determining intervention actions based on current system functionality. This segmentation avoids the combinatorial explosion of defining procedures for every possible failure combination, as each part can be solved independently rather than requiring exhaustive scenario planning.
Solution Approach 2:
The patent changes the fundamental parameter from component-based failure modes to function-based system states. Instead of defining procedures for specific component failures (which leads to combinatorial complexity), the system evaluates current system functionality and determines interventions based on what functions are still operational. This parameter change transforms an intractable combinatorial problem into a manageable functional assessment problem.
2Ease of operation
If step-by-step instructions are provided for multiple system failures, then operators receive detailed guidance, but the instructions become overwhelming and contradictory when too many systems fail simultaneously
Solution Approach 1:
The patent extracts the essential information needed for operator decision-making by focusing solely on current system functionality rather than presenting all possible failure scenarios. The system state graph identifies which systems are operational and which are not, and interventions are derived only from the functional status. This extraction eliminates overwhelming details about failed components and presents only the critical functional information operators need to make decisions.
Solution Approach 2:
Instead of telling operators what to do based on what has failed (traditional approach that creates overwhelming instructions), the patent inverts the approach by determining interventions based on what is still working. The system asks 'what functions can still be performed?' rather than 'what components have failed?', fundamentally changing the information presentation from a list of problems to a set of operational capabilities that guide intuitive decision-making.
3Reliability
If automated diagnostic systems attempt to cover all possible failure scenarios, then comprehensive coverage is achieved, but the system becomes non-deterministic and produces illogical outputs in complex scenarios
Solution Approach 1:
The patent performs preliminary action by pre-defining the system state graph with all possible system states and their functional implications before any failure occurs. The graph structure encodes the logical relationships between system components and functions in advance, allowing the automated system to deterministically evaluate current functionality and derive logical interventions without needing to predict or enumerate all possible failure scenarios. This preliminary structuring ensures consistent logical output regardless of the complexity or number of simultaneous failures.
Data Source
AI summary
The non-limiting technology described herein is a failure managing framework for complex systems that determines and restores functionality of failing systems and sub-systems using a function-based intervention approach having ontological content such as provided in a System State Graph directed graph. An integration framework allows integration of multiple intervention definition paradigms and selects the best for the current scenario; modifies procedures according to current context by encapsulating operator's tacit knowledge; provides an additional safety net during application of intervention and allows both autonomous operations and assistance to a human operator in the loop.