Rotating Component Defect Detection via Clearance Signal Segmentation
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Solution Overview
Problem
Conventional systems for monitoring turbine shroud assemblies fail to detect damages and defects, leading to potential failures due to metal fatigue and misalignment issues.
Innovation Solution
A method and system that process clearance signals between a rotating component and a stationary casing to determine signed average power values and resultant values, identifying defects or potential defects by shifting signal windows and generating a resultant value signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clearance monitoring systems are used, then clearance between shroud assembly and casing can be measured, but damages and defects in the shroud assembly cannot be detected
Solution Approach 1:
The clearance signal is divided into multiple windows, with each window analyzed separately to compute signed average power values. This segmentation allows detection of localized defects by examining specific portions of the rotational cycle, enabling the system to identify defects that would be masked in a overall clearance measurement.
Solution Approach 2:
The system dynamically adjusts monitoring by iteratively shifting signal windows and recalculating resultant values as the rotating component turns. This dynamic approach transforms static clearance measurements into a time-varying analysis that can detect defects at different angular positions, converting a simple measurement system into an active defect detection system.
2Reliability
If signal windows are iteratively shifted to detect defects, then defect detection capability is improved, but computational complexity increases
Solution Approach 1:
Instead of analyzing the entire clearance signal continuously, the system applies partial action by focusing computational effort on specific signal windows that are most likely to contain defect information. By selectively analyzing portions of the signal and using iterative shifting rather than continuous processing, the system achieves effective defect detection with reduced computational burden compared to exhaustive signal analysis.
Data Source
AI summary
A method is presented. The method includes selecting a first window of signals and a second window of signals from clearance signals representative of clearances between a rotating component and a stationary casing surrounding the rotating component, determining a first signed average power value corresponding to the first window of signals, and a second signed average power value corresponding to the second window of signals, determining a resultant value based upon the first signed average power value and the second signed average power value, and determining one or more defects or potential defects in the rotating component based upon the resultant value.