Gas Turbine Blade Crack Detection Using Chirp Fourier Transform
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Solution Overview
Problem
Existing methods for monitoring vibrations in gas turbine blades fail to effectively separate the required signal from background noise, leading to inefficiencies in detecting defects like cracks, and require multiple sensors for complex engines, increasing downtime and costs.
Innovation Solution
A method involving short-time chirp Fourier transform to isolate periodic responses by varying rotational velocity, combined with data sampling and peak detection techniques to identify true resonance peaks, allowing for the detection of defects without excessive noise interference and using fewer sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vibration measurement methods are used to monitor blade health, then defects can be detected, but the required signal cannot be effectively separated from background noise
Solution Approach 1:
The patent applies dynamics by varying the rotational velocity of the shaft during measurement. This dynamic approach allows the instantaneous frequency of engine orders to change over time, enabling the signal to pass through different frequency regions and separate from stationary noise components. The time-varying frequency characteristic is key to achieving signal isolation from background noise.
Solution Approach 2:
The patent utilizes periodic action by exploiting the periodic nature of engine orders relative to shaft rotation. By performing measurements during acceleration or deceleration cycles, the method captures periodic vibration responses at varying frequencies, allowing multiple measurements to be combined to improve signal-to-noise ratio while maintaining defect detection reliability.
2Reliability
If multiple sensors are used to monitor each row of blades in complex gas turbine engines, then comprehensive health monitoring is achieved, but device complexity and costs increase
Solution Approach 1:
The patent applies universality by designing a measurement system that can monitor multiple blade rows simultaneously using a single sensor. The method captures vibration responses from different blade rows during shaft acceleration/deceleration, and through signal processing separates the responses. This multi-functional approach allows one sensor to perform the work of multiple sensors, reducing device complexity while maintaining comprehensive health monitoring coverage.
Solution Approach 2:
The patent uses copying by creating virtual measurements for different blade rows from a single physical sensor measurement. Through signal processing techniques, the system reconstructs vibration responses that would correspond to multiple sensor positions, effectively copying the measurement capability without requiring additional physical sensors.
3Reliability
If comprehensive vibration analysis is performed on all blade modes, then all potential defects are detected, but the amount of data to process increases significantly
Solution Approach 1:
The patent applies extraction by isolating specific periodic responses corresponding to blade resonance from the overall vibration signal. During shaft acceleration or deceleration, the method identifies and extracts vibration components that match blade natural frequencies, separating them from other signal components. This selective extraction reduces the data processing burden by focusing only on relevant defect-indicating signals while maintaining comprehensive defect detection capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively isolates and analyzes vibro-acoustic signals to detect defects like cracks in gas turbine blades, reducing downtime and costs by improving signal-to-noise ratio and enabling more efficient health monitoring with fewer sensors.
Implementation Method 1
transforming the signal using a short-time chirp Fourier transform, thereby isolating the selected periodic response
Implementation Method 2
The frequency of the signals is related to the angular velocity of the respective shaft and hence the engine speed. These rotation-periodic signals are conventionally known as order components. As the angular speed of rotation increases typically the order components similarly increase in frequency.
Data Source
AI summary
Engine health monitoring is used to assess the health of an engine, such as a gas turbine engine. Blades mounted on a shaft produce a modal response when excited. The shaft has an order related component that varies with the rotational velocity of the shaft. Modal responses are increased when the natural frequency range of the selected blade mode intersects with one of the order related components. By applying a short time chirp-Fourier transform with a frequency speed that is a function of a rate of change in the rotational velocity of the shaft a selected signal can be isolated. Cracks in the blades can be detected from the isolated signal.