Dynamic Reliability Model for Aerial Vehicle Mission Planning
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Solution Overview
Problem
Existing reliability modeling techniques for aerial vehicles are static and do not account for real-time changes or external factors, limiting their effectiveness in mission planning and maintenance.
Innovation Solution
The use of dynamic reliability models combined with machine learning algorithms to characterize the reliability of aerial vehicles, update models in real-time based on component reliability and weather factors, and compute mission success probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static reliability modeling techniques are used for aerial vehicles, then the modeling process is simple and straightforward, but the models cannot account for real-time changes or external factors, limiting their effectiveness in mission planning and maintenance
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from static reliability models to dynamic reliability models that continuously update based on real-time component data and external factors. The system dynamically adjusts reliability assessments as new information becomes available, allowing the model to adapt to changing conditions during mission planning and execution.
Solution Approach 2:
The patent implements Feedback by incorporating real-time component reliability data and external factor information into the dynamic reliability model. The system continuously receives feedback from sensor data, maintenance records, and environmental conditions, then updates the reliability assessment accordingly, creating a closed-loop system that improves accuracy over time.
2Measurement precision
If dynamic reliability models with machine learning are implemented, then real-time accurate assessments of vehicle reliability are provided, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies Self-service by enabling the dynamic reliability model to automatically update itself using machine learning algorithms that process incoming data without requiring manual intervention. The system self-adjusts reliability assessments based on patterns learned from historical data and real-time inputs, reducing the need for complex manual analysis while maintaining high precision.
3Reliability
If real-time updates based on component reliability data and weather factors are performed, then the reliability assessments remain current and accurate, but the data processing and model updating computational load increases
Solution Approach 1:
The patent applies Partial or excessive action by implementing selective updating of the dynamic reliability model, focusing computational resources on processing the most critical component data and external factors that have the greatest impact on reliability assessments. This approach maintains current and accurate reliability information while optimizing energy consumption by avoiding unnecessary processing of less significant data.
Data Source
AI summary
Systems and methods are disclosed herein for influencing mission planning, vehicle maintenance, and identifying an aerial vehicle that provide a greatest probability of mission success. In some examples, a dynamic reliability model of dynamic reliability models can be updated based on at least component reliability data indicative of a reliability of a component of the aerial vehicle of aerial vehicles. Each dynamic reliability model can characterize a reliability of one of the aerial vehicles. Each dynamic reliability model can be executed to compute an indication of vehicle reliability for each aerial vehicle. A mission of success probability for each aerial vehicle can be computed based on a respective indication of vehicle reliability. A given aerial vehicle of the aerial vehicles can be identified for implementing the mission based on an evaluation of the mission of success probability for each aerial vehicle of the aerial vehicles.