A Review of Selected Works on Crack Identification

2004 ◽  
pp. 25-46 ◽  
Author(s):  
Kurt Bryan ◽  
Michael S. Vogelius
Keyword(s):  
Crack Identification

Rotor Crack Identification Based on Mode Shape and Derivative Order Using Combination Resonances

2019 ◽  
Author(s):  
Izabela Batista da Silva ◽  
Paulo Costa Porto de Figueiredo Barbosa ◽  
Aldemir Ap Cavalini Jr ◽  
Valder Steffen Jr ◽  
Nicolò Bachschmid
Keyword(s):  
Mode Shape ◽  
Crack Identification ◽  
Derivative Order

scholarly journals SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

2021 ◽  
Vol 11 (6) ◽  
pp. 2784
Author(s):  
Shahnaz TayebiHaghighi ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Rolling Element Bearing ◽  
Crack Identification ◽  
Original Signal ◽  
Signal Modeling ◽  
Residual Signal ◽  
Average Accuracy ◽  
Self Tuning ◽  
The Difference

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

Crack Identification of a Beam by Measurements of Natural Frequencies

2003 ◽  
Vol 42 (Part 1, No. 3) ◽  
pp. 1341-1347 ◽  
Author(s):  
Hai-Ping Lin
Keyword(s):  
Natural Frequencies ◽  
Crack Identification

scholarly journals Structural Crack Identification in Railway Prestressed Concrete Sleepers Using Dynamic Mode Shapes

Proceedings ◽  
2018 ◽  
Vol 2 (16) ◽  
pp. 1139
Author(s):  
Rims Janeliukstis ◽  
Sandris Rucevski ◽  
Sakdirat Kaewunruen
Keyword(s):  
Prestressed Concrete ◽  
Mode Shapes ◽  
Crack Identification ◽  
Dynamic Mode ◽  
Critical Component ◽  
Safety Critical ◽  
Railway Tracks

Railway prestressed concrete sleepers are a structural and safety-critical component in railway tracks. [...]

scholarly journals In Vivo X-ray Computed Tomography Investigations of Crack Damage Evolution of Cemented Waste Rock Backfills (CWRB) under Uniaxial Deformation

Minerals ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 539 ◽  
Author(s):  
Yu Wang ◽  
Changhong Li ◽  
Zhiqiang Hou ◽  
Xuefeng Yi ◽  
Xiaoming Wei
Keyword(s):  
Computed Tomography ◽  
Damage Evolution ◽  
Waste Rock ◽  
Crack Identification ◽  
Mining Operations ◽  
Cracking Behavior ◽  
Value Analysis ◽  
X Ray ◽  
Waste Rocks ◽  
X Ray Computed

Cemented waste rock backfill (CWRB), which is a mixture of tailings, waste rock, cement, and water, is subjected to combination actions in underground mining operations and has been widely used in deep resource mining. While the strength requirement and macroscopic deformation behaviors of CWRB have been well studied, the mesoscopic damage evolution mechanisms are still not well understood. In this work, a CWRB sample with a waste rock proportion of 30% was studied with a uniaxial compression test under tomographic monitoring, using a 450 kV industrial X-ray computed tomography (CT). Clear CT images, CT value analysis, crack identification, and extraction reveal that CWRB damage evolution is extremely inhomogeneous and affected by the waste rock size, shape, and distribution. Furthermore, the crack initiation, propagation, and coalescence behaviors are limited to the existing waste rocks. When deformation grows to a certain extent, the cracks demonstrate an interlocking phenomenon and their propagation paths are affected by the waste rocks, which may improve the ability to resist compressive deformation. Volumetric dilatancy caused by the damage and cracking behavior has closed a link with the meso-structural changes, which are controlled by the interactions between the waste rocks and the cemented tailing paste.

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