TY - JOUR
T1 - Abnormal events recognition and classification for pipeline monitoring systems based on vibration analysis and artificial neural networks
AU - Chen, Bin
AU - Cheng, Xiaobin
AU - Yan, Zhaoli
AU - Yang, Jun
PY - 2013
Y1 - 2013
N2 - Pipelines become the principal means of oil and gas transportation. The leakage usually takes place due to some natural or artificial damages and causes loss of life and properties. Now a pre-warning system based on distributed optical fiber sensor has been proposed and deployed in China. Now, its following key problem is how to recognize and classify damage activities along with pipeline, such as ramming, rotor working, manual digging, well knocking, and mechanical execution. This paper involves in-depth study on recognition method for this system. Firstly, original vibration signal is pre-processed and segmented according to energy threshold and sliding window. Through statistical and short-time Fourier transform (STFT) analysis in time and frequency domain, energy ratios and frequency centroid are extracted as feature vectors, which can describe and distinguish distribution characteristics of each vibration event effectively. At classification, event set is divided firstly into discrete and continuous events with kurtosis, which can decrease classified event dimension and improve recognition accuracy. Then BP artificial neural network is applied to identify damage and non-threatening events. Experiment results show that proposed algorithm can differentiate discrete events with accuracy rate of 99%, while continuous events with 97.5%.
AB - Pipelines become the principal means of oil and gas transportation. The leakage usually takes place due to some natural or artificial damages and causes loss of life and properties. Now a pre-warning system based on distributed optical fiber sensor has been proposed and deployed in China. Now, its following key problem is how to recognize and classify damage activities along with pipeline, such as ramming, rotor working, manual digging, well knocking, and mechanical execution. This paper involves in-depth study on recognition method for this system. Firstly, original vibration signal is pre-processed and segmented according to energy threshold and sliding window. Through statistical and short-time Fourier transform (STFT) analysis in time and frequency domain, energy ratios and frequency centroid are extracted as feature vectors, which can describe and distinguish distribution characteristics of each vibration event effectively. At classification, event set is divided firstly into discrete and continuous events with kurtosis, which can decrease classified event dimension and improve recognition accuracy. Then BP artificial neural network is applied to identify damage and non-threatening events. Experiment results show that proposed algorithm can differentiate discrete events with accuracy rate of 99%, while continuous events with 97.5%.
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U2 - 10.1121/1.4800816
DO - 10.1121/1.4800816
M3 - Conference article
AN - SCOPUS:84878964554
SN - 1939-800X
VL - 19
JO - Proceedings of Meetings on Acoustics
JF - Proceedings of Meetings on Acoustics
M1 - 055021
T2 - 21st International Congress on Acoustics, ICA 2013 - 165th Meeting of the Acoustical Society of America
Y2 - 2 June 2013 through 7 June 2013
ER -