Laboratory tests on local damage detection for jacket-type offshore structures using optical FBG sensors based on statistical approaches

2016 ◽  
Vol 124 ◽  
pp. 94-103 ◽  
Author(s):  
Jin-Hak Yi
Keyword(s):  
Damage Detection ◽  
Laboratory Tests ◽  
Offshore Structures ◽  
Local Damage ◽  
Fbg Sensors ◽  
Statistical Approaches

Non-dominated solution set based on time–frequency infograms for local damage detection of rotating machines

2019 ◽  
Vol 92 ◽  
pp. 213-227 ◽  
Author(s):  
Xingxing Jiang ◽  
Juanjuan Shi ◽  
Weiguo Huang ◽  
Zhongkui Zhu
Keyword(s):  
Damage Detection ◽  
Solution Set ◽  
Local Damage ◽  
Rotating Machines ◽  
Time Frequency

scholarly journals Nonnegative Matrix Factorization of time frequency representation of vibration signal for local damage detection – comparison of algorithms

2018 ◽  
Vol 29 ◽  
pp. 00010
Author(s):  
Jacek Wodecki
Keyword(s):  
Damage Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Single Channel ◽  
Classical Method ◽  
Nonnegative Matrix ◽  
Vibration Signal ◽  
Local Damage ◽  
Time Frequency ◽  
Frequency Representation

Local damage detection in rotating machine elements is very important problem widely researched in the literature. One of the most common approaches is the vibration signal analysis. Since time domain processing is often insufficient, other representations are frequently favored. One of the most common one is time-frequency representation hence authors propose to separate internal processes occurring in the vibration signal by spectrogram matrix factorization. In order to achieve this, it is proposed to use the approach of Nonnegative Matrix Factorization (NMF). In this paper three NMF algorithms are tested using real and simulated data describing single-channel vibration signal acquired on damaged rolling bearing operating in drive pulley in belt conveyor driving station. Results are compared with filtration using Spectral Kurtosis, which is currently recognized as classical method for impulsive information extraction, to verify the validity of presented methodology.

Damage detection in offshore structures using neural networks

2010 ◽  
Vol 23 (1) ◽  
pp. 131-145 ◽  
Author(s):  
Ahmed A. Elshafey ◽  
Mahmoud R. Haddara ◽  
H. Marzouk
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Offshore Structures
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