Blade Crack Detection using Blade Tip Timing

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk.

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An improved analytical dynamic model for rotating blade crack: With application to crack detection indicator analysis

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/14613484211012602 ◽

2021 ◽

pp. 146134842110126

Author(s):

Laihao Yang ◽

Meng Ma ◽

Shuming Wu ◽

Xuefeng Chen ◽

Ruqiang Yan ◽

...

Keyword(s):

Feature Extraction ◽

Dynamic Model ◽

Crack Detection ◽

Failure Modes ◽

Numerical Study ◽

Analytical Models ◽

Crack Model ◽

Rotating Blade ◽

Vibration Responses ◽

Blade Crack

Rotating blade is one of the most important components for turbomachinery. Blade crack is one of the most common and dangerous failure modes for rotating blade. Therefore, the fault mechanism and feature extraction of blade crack are vital for the safety assurance of turbomachinery. This study is aimed at the nonlinear dynamic model of rotating blade with transverse crack and the prior feature extraction of blade crack faults based on the vibration responses. First and foremost, a high-fidelity breathing crack model (HFBCM) for rotating blade is proposed on the basis of criterion for stress states at crack section. Since HFBCM is physically deduced from the perspective of energy dissipation and the coupling between centrifugal stress and bending stress is considered, the physical interpretability and the accuracy of the crack model are enhanced comparing with conventional models. The validity of the proposed HFBCM is verified through the comparison study among HFBCM, conventional crack models, and finite element-based contact crack model (FECCM). It is suggested that HFBCM behaves best among the analytical models and matches well with FECCM. With the proposed HFBCM, the nonlinear vibration responses are investigated, and four types of blade crack detection indicators for rotating blade and their quantification method are presented. The numerical study manifests that all these indicators can well characterize the occurrence and severity of crack faults for rotating blade. It is indicated that these indicators can serve as the crack-monitoring indexes.

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Identification of Asynchronous Blade Vibration Parameters by Linear Regression of Blade Tip Timing Data

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4038880 ◽

2018 ◽

Vol 140 (7) ◽

Cited By ~ 5

Author(s):

Abbas Rohani Bastami ◽

Pedram Safarpour ◽

Arash Mikaeily ◽

Mohammad Mohammadi

Keyword(s):

Health Monitoring ◽

Crack Detection ◽

Turbine Blades ◽

Sampling Rate ◽

Health Indicators ◽

Least Mean Square ◽

Blade Vibration ◽

Monitoring Method ◽

Blade Tip ◽

Vibration Parameters

Fracture of blades is usually catastrophic and creates serious damages in the turbomachines. Blades are subjected to high centrifugal force, oscillating stresses, and high temperature which makes their life limited. Therefore, blades should be checked and replaced at specified intervals or utilize a health monitoring method for them. Crack detection by nondestructive tests can only be performed during machine overhaul which is not suitable for monitoring purposes. Blade tip timing (BTT) method as a noncontact monitoring technique is spreading for health monitoring of the turbine blades. One of the main challenges of BTT method is identification of vibration parameters from one per revolution samples which is quite below Nyquist sampling rate. In this study, a new method for derivation of blade asynchronous vibration parameters from BTT data is proposed. The proposed method requires only two BTT sensors and applies least mean square algorithm to identify frequency and amplitude of blade vibration. These parameters can be further used as blade health indicators to predict defect growth in the blades. Robustness of the proposed method against measurement noise which is an important factor has been examined by numerical simulation. An experimental test was conducted on a bladed disk to show efficiency of the proposed method.

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Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

Shock and Vibration ◽

10.1155/2015/954932 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Bingbing Hu ◽

Bing Li

Keyword(s):

Stochastic Resonance ◽

Crack Detection ◽

Current Method ◽

Ensemble Empirical Mode Decomposition ◽

Centrifugal Fan ◽

Blade Vibration ◽

Transform Method ◽

Mode Decomposition ◽

Centrifugal Fans ◽

Blade Crack

Centrifugal fans are widely used in various industries as a kind of turbo machinery. Among the components of the centrifugal fan, the impeller is a key part because it is used to transform kinetic energy into pressure energy. Crack in impeller’s blades is one of the serious hidden dangers. It is important to detect the cracks in the blades as early as possible. Based on blade vibration signals, this research applies an adaptive stochastic resonance (ASR) method to diagnose crack fault in centrifugal fan. The ASR method, which can utilize the optimization ability of the grid search method and adaptively realize the optimal stochastic resonance system matching input signals, may weaken the noise and highlight weak characteristic and thus can diagnose the fault accurately. A centrifugal fan test rig is established and experiments with three cases of blades are conducted. In comparison with the ensemble empirical mode decomposition (EEMD) analysis and the traditional Fourier transform method, the experiment verified the effectiveness of the current method in blade crack detection.

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Blade and Shaft Crack Detection Using Torsional Vibration Measurements Part 2: Resampling to Improve Effective Dynamic Range

Noise & Vibration Worldwide ◽

10.1260/0957456011498281 ◽

2001 ◽

Vol 32 (2) ◽

pp. 23-26 ◽

Cited By ~ 8

Author(s):

Kenneth P. Maynard ◽

Martin Trethewey

Keyword(s):

Torsional Vibration ◽

Crack Detection ◽

Natural Frequencies ◽

Dynamic Range ◽

Detection System ◽

Signal To Noise Ratio ◽

Development Project ◽

Experimental Apparatus ◽

Effective Dynamic ◽

Blade Crack

The primary goal of the development project was to demonstrate the feasibility of detecting changes in blade bending natural frequencies (such as those associated with a blade crack) on a turbine using non-contact, non-intrusive measurement methods. The approach was to set up a small experimental apparatus, develop a torsional vibration detection system, and maximize the dynamic range and the signal to noise ratio. The results of the testing and analysis clearly demonstrated the feasibility of using torsional vibration to detect the change in natural frequency of a blade due to a change in stiffness such as those associated with a blade crack. However, it was found that harmonics of shaft operating speed, created as an unwanted artifact of the measurement method, resulted in spectral regions in which the effective dynamic range was inadequate to detect low-level torsional vibration associated with the natural frequencies. The loss of effective dynamic range was due to the “skirts” created by the sampling window. Application of order resampling, followed by frequency resampling, to the torsional vibration waveform increased the effective dynamic range and improved the ability to identify shaft torsional and blade bending natural frequencies.

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