Gas Turbine Inspection Drones for Adaptive Abnormality Detection
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Solution Overview
Problem
Gas turbine engine inspections are often invasive, time-consuming, and expensive due to the need for manual intervention and engine removal, which hinders efficient identification and addressing of engine component distress.
Innovation Solution
An inspection system utilizing a team of terrestrial drones equipped with inspection sensors, where a processor choreographs their movement along specific paths to collect data and adaptively deviate from paths to confirm abnormalities or address incomplete inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used, then inspection accuracy can be maintained, but inspection time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated drone system equipped with sensors (cameras, LIDAR, thermal sensors). The drones autonomously navigate engine components and collect inspection data, eliminating the need for human inspectors to physically access and examine each component manually.
Solution Approach 2:
The inspection system performs self-assessment through automated data collection and analysis. The drones independently navigate, capture inspection data, and the system automatically processes the information to identify abnormalities, reducing reliance on continuous human intervention throughout the inspection process.
2Reliability
If comprehensive inspection paths are implemented, then inspection completeness improves, but inspection time increases
Solution Approach 1:
The inspection system dynamically adjusts drone paths in real-time based on detected abnormalities. When the system identifies a potential issue, it automatically modifies the inspection route to focus on that area, allowing comprehensive coverage of critical components while reducing time spent on areas of lower concern.
Solution Approach 2:
The inspection process is divided into multiple drone units, each responsible for specific engine sections. This segmentation allows parallel inspection of different components simultaneously, maintaining comprehensive coverage while reducing total inspection time through concurrent operations.
3Reliability
If adaptive path deviation is implemented to confirm abnormalities, then detection reliability improves, but inspection complexity increases
Solution Approach 1:
The system implements feedback loops where inspection data is continuously analyzed and used to adjust subsequent inspection paths. When abnormalities are detected, the system feeds this information back into path planning algorithms, which automatically generate revised routes to re-examine suspicious areas with different sensor angles or methods for confirmation.
Data Source
AI summary
An inspection system for a gas turbine engine includes a team of terrestrial drones each equipped with at least one inspection sensor and at least one processor. The processor is configured to choreograph operation of the terrestrial drones to each move along an associated drone-specific inspection path and collectively traverse an area of interest in a gas turbine engine; and operate the inspection sensor of each of the terrestrial drones to collect inspection data along the associated drone-specific inspection path.