Inspection UAV Planning Using Flight History for Aircraft Reliability
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Solution Overview
Problem
Current aircraft inspection methods for UAVs are time-consuming, costly, and inefficient, especially for smaller, lower-cost platforms, as they rely on rigid and pre-calculated processes without data-driven guidance, which may not ensure safety or reliability.
Innovation Solution
The use of an inspection UAV (I-UAV) equipped with sensors to assess the state of utility UAVs (U-UAVs) by employing models of failure modes, flight history data, and AI-driven inspection planning to create dynamic and data-driven inspection plans, allowing for autonomous or hybrid inspection processes that focus on high reliability and low operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rigid pre-calculated inspection processes are used, then safety and reliability are maintained, but inspection time and cost increase excessively
Solution Approach 1:
The inspection process transitions from static pre-calculated procedures to dynamic adaptive inspection. The system continuously adjusts inspection parameters, sensor configurations, and flight paths based on real-time data from flight history and actual component conditions, allowing inspections to be both thorough and time-efficient.
Solution Approach 2:
The system changes inspection parameters dynamically based on analyzed data. Inspection depth, sensor types deployed, and focus areas are adjusted according to flight history patterns and detected anomalies, rather than following fixed predetermined parameters for all inspections.
2Reliability
If comprehensive inspection processes are used, then reliability is ensured, but operational cost increases
Solution Approach 1:
The inspection system applies different levels of inspection intensity to different components based on their risk profiles and flight history. High-risk components with problematic histories receive comprehensive inspection, while low-risk components receive minimal inspection, optimizing resource allocation and reducing overall costs.
Solution Approach 2:
The system performs only the necessary portion of inspection required to ensure reliability. By using predictive analytics to identify specific components needing attention, the system avoids performing excessive inspections on already healthy components, reducing operational costs while maintaining safety.
3Productivity
If data-driven dynamic inspection planning is implemented, then inspection efficiency improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary inspection planning layer between data collection and execution. This planning module processes flight history and component data to generate optimized inspection sequences, acting as a mediator that translates raw data into actionable inspection tasks, thereby managing complexity while improving efficiency.
Solution Approach 2:
The system replaces manual inspection planning and execution with automated intelligent systems. AI algorithms analyze flight data and generate inspection plans, substituting human mechanical processes with computational systems that handle complexity more efficiently.
4Loss of energy
If smaller lower-cost UAV platforms are used, then operational cost decreases, but inspection capability and reliability may be compromised
Solution Approach 1:
The inspection system is designed to be universal and adaptable across different UAV platforms. By using software-based inspection planning and multiple sensor types that can detect various failure modes, the system maintains high reliability regardless of the specific hardware platform, allowing smaller cheaper UAVs to perform reliable inspections.
Data Source
AI summary
Pre-flight and episodic aircraft inspections to ensure operational safety and effectiveness are made faster, less costly and more effective by utilizing mobile model and data-driven sensor platforms inspecting known-state target vehicles. For autonomous aircraft, an inspection unmanned aerial vehicle (I-UAV) containing sensors inspects a utility unmanned aerial vehicle (U-UAV). Failure and system models of the U-UAV and the flight history of the U-UAV carried in the sensor data captured by the U-UAV's aircraft health management system are combined to guide the I-UAV to appropriately attend to the systems of the U-UAV and to drive the U-UAV systems into target states that will reveal otherwise hidden or latent failures.