Control Effector Health Reasoning for Fault Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle health management systems face challenges in accurately determining the lost and remaining functional capabilities of control effectors under severe environmental conditions, often resulting in inadequate precision and undue complexity, especially in vehicles with power, weight, and size constraints.
Innovation Solution
A system and method that processes command and sensor data to generate health data for control effectors, using a reasoner to selectively indict and clear faults, determine failures, and determine the usable range of control effector commands, incorporating a test module and reasoner to handle diagnostic complexities like ambiguity and false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional health monitoring systems are used in vehicles with power, weight, and size constraints, then the system complexity is reduced, but the measurement precision and fault isolation capability deteriorate
Solution Approach 1:
The patent introduces a reasoner as an intermediary component that processes health data and generates capability assessments. The reasoner acts as a mediator between the simple onboard sensors and the complex fault analysis requirements, enabling precise fault isolation without adding complex hardware. The reasoner synthesizes information from multiple sources and applies diagnostic logic to determine control effector capabilities.
Solution Approach 2:
The patent replaces complex mechanical fault isolation systems with an information-processing approach. Instead of using additional physical sensors and mechanical diagnostic equipment, the system uses a reasoner that processes existing health data through logical inference and analysis algorithms to achieve precise fault identification and capability determination.
2Measurement precision
If comprehensive health monitoring is implemented to accurately determine control effector capabilities, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The reasoner serves multiple functions within a single component: it processes health data, identifies faults, determines control effector capabilities, and provides recommendations. This multi-functional approach achieves comprehensive health monitoring accuracy without requiring separate complex systems for each function, thereby limiting the increase in overall device complexity.
3Measurement precision
If fault isolation is attempted to determine control effector health, then the measurement precision improves, but the loss of time increases due to diagnostic complexities
Solution Approach 1:
The system performs preliminary analysis by continuously processing health data through the reasoner, which maintains an updated understanding of control effector capabilities. This preliminary action allows the system to have fault isolation and capability determination already prepared or quickly determined when needed, reducing the time loss during critical diagnostic situations.
4Adaptability or versatility
If adaptive reconfiguration is implemented to maintain functionality under degraded conditions, then the adaptability improves, but the device complexity increases
Solution Approach 1:
The patent implements dynamic adaptability where the control system can reconfigure itself based on real-time health assessments from the reasoner. The system transitions from static control architecture to a dynamic one that automatically adjusts control effector usage, command distribution, and system configuration based on current capabilities, enabling adaptability through software-controlled dynamics rather than complex hardware reconfiguration.
Data Source
AI summary
A system and method for determining the response capabilities of a control effector are provided. Command data and sensor data associated with the control effector are processed to generate control effector health data representative of control effector health. The control effector health data are processed in a reasoned. The reasoned is configure to selectively indict and clear one or more faults, determine one or more failures that cause indicted faults, and determine, based on the one or more determined failures, a usable range of control effector commands to which the control effector can respond.