Improvements on FEA with a two-step simulation of experimental procedures in turbine blade crack detection in sonic IR NDE

2013 ◽  
Author(s):  
Ding Zhang ◽  
Xiaoyan Han ◽  
Golam Newaz ◽  
Lawrence D. Favro ◽  
Robert L. Thomas
Keyword(s):  
Turbine Blade ◽  
Crack Detection ◽  
Blade Crack ◽  
Sonic Ir

Blade Crack Detection using Blade Tip Timing

2021 ◽  
pp. 1-1
Author(s):  
Shuming Wu ◽  
Zengkun Wang ◽  
Haoqi Li ◽  
Zhibo Yang ◽  
Shaohua Tian ◽  
...  
Keyword(s):  
Crack Detection ◽  
Blade Tip ◽  
Blade Crack

A Method of Crack Detection in the Turbine Blade Using Digital Holographic Microscopy (DHM)

2011 ◽  
Author(s):  
Naoya Tada ◽  
Seiichi Hamada ◽  
Tetsuo Teramae ◽  
Shin Yoshino ◽  
Takehiko Suzuki
Keyword(s):  
Finite Element ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Crack Detection ◽  
Mechanical Load ◽  
Combined Cycle ◽  
Digital Holographic Microscopy ◽  
Finite Element Analyses ◽  
Blade Surface ◽  
Holographic Microscopy

The authors proposed an NDE method of detecting the crack developed in the turbine blade by means of digital holographic microscopy (DHM). UT procedures sometimes have limitations in particular cases of in-service-inspections such as the detection and sizing of a creep crack developed in the air-cooled casting blade of the combined cycle gas turbine. The local displacement at the blade surface near the crack during a mechanical loading is different from that of a non-cracked blade. This small different can be detected by DHM. The authors discussed whether this difference affected by the mechanical load between the cracked blade and the non-cracked is detectable by means of DHM or not. In this paper, the authors verified the practical validity of the proposed NDE method using the finite element analyses.

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

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.

scholarly journals Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
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.

G0301306 Effects of rust in the crack face on crack detection based on Sonic-IR method

2015 ◽  
Vol 2015 (0) ◽  
pp. _G0301306--_G0301306-
Author(s):  
Y. HARAI ◽  
Y. IZUMI ◽  
H. TANABE ◽  
T. TAKAMATSU ◽  
T. SAKAGAMI
Keyword(s):  
Crack Detection ◽  
Crack Face ◽  
Sonic Ir

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

2001 ◽  
Vol 32 (2) ◽  
pp. 23-26 ◽  
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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