Bladed Rotor Inspection Data Reduction for Faster Defect Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current inspection methods for bladed rotors in gas turbine engines are inefficient and costly, requiring significant manual inputs and conservative estimates due to the need for creating models from scratch to analyze damage and determine acceptable repairs, and they often fail to accurately reflect the specific shape of the rotor being inspected.
Innovation Solution
A method and system that utilize a processor to receive scanner data, generate section files based on a point cloud, determine defects, and assess whether they meet serviceable limits, allowing for the transmission of relevant data to an analysis system for further processing, which includes generating repair blend profiles and reducing data size by up to 100 times for efficient transfer and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If models are created from scratch to analyze damage and determine acceptable repairs, then analysis accuracy is improved, but analysis time and cost increase significantly
Solution Approach 1:
The system pre-generates a library of blend profiles and stores them in a database before inspection is needed. During inspection, the processor automatically retrieves and applies the appropriate pre-generated blend profile to the detected defect, eliminating the time-consuming process of creating models from scratch while maintaining analysis accuracy
Solution Approach 2:
The system prepares multiple predetermined blend profiles in advance that can accommodate various defect types and sizes. By having these profiles ready beforehand in the database, the system cushions against the time loss that would otherwise occur during emergency or urgent inspection scenarios where quick analysis is needed
2Reliability
If manual inputs and conservative estimates are used to account for potential variations, then reliability is improved, but productivity decreases
Solution Approach 1:
The processor automatically retrieves defect measurements from the point cloud data and self-applies the appropriate blend profile without requiring manual human intervention. The system serves itself by automatically comparing the defect against serviceable limits and determining acceptability, thereby maintaining reliability through consistent application of criteria while dramatically improving productivity
Solution Approach 2:
The system changes the state of blend profiles from manual parameters requiring human input to automated digital parameters stored in a database. The processor dynamically selects and applies the correct blend profile based on defect characteristics, transforming the process from manual estimation to automated parameter-based determination, thus improving both reliability and productivity
3Loss of information
If large scanner data files are transferred for analysis, then data completeness is improved, but data transfer time and processing overhead increase
Solution Approach 1:
The processor extracts only the essential defect-related information from the complete point cloud data and transfers only this extracted subset to the analysis system. By taking out and transferring only the necessary defect coordinates and characteristics rather than the entire scanner data set, the system maintains data completeness for analysis while dramatically reducing data transfer time and overhead
Data Source
Figure 1A
Figure 1B
Figure 2~4
AI summary
A method can comprise: receiving, via a processor (286), scanner data from an inspection system (285) for a bladed rotor (100), the scanner data including a point cloud defining an inspected bladed rotor, the scanner data including a first data size; generating, via the processor (286), a data set including section files spaced apart along a span of a blade (103) based on the point cloud; determining, via the processor (286), a defect (140) on the blade (103) of the inspected bladed rotor based on the data set; and determining whether the defect (140) meets serviceable limits.