More Web Proxy on the site http://driver.im/

research-article

Study of the acoustic noise in pipelines carrying oil products in a refinery establishment

Authors:

George Othon Glentis,

Kostas Angelopoulos,

Spyridon Nikolaidis,

Georgios Panagiotis Kousiopoulos,

Dimitrios Porlidas,

Dimitrios GkountisAuthors Info & Claims

PCI '19: Proceedings of the 23rd Pan-Hellenic Conference on Informatics

Pages 34 - 41

https://doi.org/10.1145/3368640.3368685

Published: 28 November 2019 Publication History

Abstract

The monitoring of pipeline networks in utility or industrial distribution installations for fault detection (e.g. leaks in water or hydrocarbon pipelines) is important for economical and environmental reasons and critical from the health hazard perspective. Significant focus has been attracted over the years on the implementation of passive and active methods for the fault source localization, based on acoustic signal processing. The knowledge of the acoustic noise background's stochastic characteristics, that is inherent in such installations due to electronic or mechanical machinery induced interference is crucial for designing and deploying an efficient, acoustic signal based, fault detection and localization system. In this paper, time and frequency domain algorithmic approaches are presented for estimating the lower and higher order statistical parameters of the signals of interest, as well as power spectrum and correlation estimates. These techniques are utilized on real measurements from oil refinery pipeline systems for acoustic noise characterization as a first step for the subsequent derivation of a Time-Difference-of-Arrival (TDOA) source localization system.

References

[1]

Mohammed Y Aalsalem, Wazir Zada Khan, Wajeb Gharibi, Muhammad Khurram Khan, and Quratulain Arshad. 2018. Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. Journal of Network and Computer Applications 113 (2018), 87--97.

[2]

PCB ICP accelerometer. [n.d.]. https://www.pcb.com/products?m=352C33.

[3]

Mutiu Adesina Adegboye, Wai-Keung Fung, and Aditya Karnik. 2019. Recent advances in pipeline monitoring and oil leakage detection technologies: principles and approaches. Sensors 19, 11 (2019), 2548.

[4]

Julius S Bendat and Allan G Piersol. 2011. Random data: analysis and measurement procedures. Vol. 729. John Wiley & Sons.

[5]

Augusto Bianchini, Alessandro Guzzini, Marco Pellegrini, and Cesare Saccani. 2016. Natural Gas distribution system: overview of leak detection systems. Proceedings of the XXI Summer School Francesco Turco (2016), 123--127.

[6]

Augusto Bianchini, Alessandro Guzzini, Marco Pellegrini, and Cesare Saccani. 2018. Natural gas distribution system: A statistical analysis of accidents data. International Journal of Pressure Vessels and Piping 168 (2018), 24--38.

[7]

Anders Brandt.2011. Noise and vibration analysis: signal analysis and experimental procedures. John Wiley & Sons.

[8]

Wahyu Caesarendra and Tegoeh Tjahjowidodo. 2017. A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5, 4 (2017), 21.

[9]

Jack Capon. 1969. High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE 57, 8 (1969), 1408--1418.

[10]

National Instruments cDAQ-9174. [n.d.]. http://www.ni.com/en-us/support/model.cdaq-9174.html.

[11]

Shantanu Datta and Shibayan Sarkar. 2016. A review on different pipeline fault detection methods. Journal of Loss Prevention in the Process Industries 41 (2016), 97--106.

[12]

Aidin Ghavamian, Faizal Mustapha, BT Baharudin, and Noorfaizal Yidris. 2018. Detection, localisation and assessment of defects in pipes using guided wave techniques: a review. Sensors 18, 12 (2018), 4470.

[13]

George-Othon Glentis. 2008. A fast algorithm for APES and Capon spectral estimation. IEEE Transactions on Signal Processing 56, 9 (2008), 4207--4220.

Digital Library

[14]

S. Hamilton and B. Charalambous. 2013. Leak Detection: Technology and Implementation. IWA Publishing.

[15]

Monson H Hayes. 2009. Statistical digital signal processing and modeling. John Wiley & Sons.

[16]

Morgan Henrie, Philip Carpenter, and R Edward Nicholas. 2016. Pipeline leak detection handbook. Gulf Professional Publishing.

[17]

Hao Jin, Laibin Zhang, Wei Liang, and Qikun Ding. 2014. Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method. Journal of Loss Prevention in the Process Industries 27 (2014), 74--88.

[18]

Liwen Jing, Zhao Li, Yue Li, and Ross D Murch. 2018. Channel characterization of acoustic waveguides consisting of straight gas and water pipelines. IEEE Access 6 (2018), 6807--6819.

[19]

WJ Jobst and SL Adams. 1977. Statistical analysis of ambient noise. The Journal of the Acoustical Society of America 62, 1 (1977), 63--71.

[20]

Steven M Kay. 1988. Modern spectral estimation. Pearson Education India.

[21]

Steven M Kay. 1993. Fundamentals of statistical signal processing. Prentice Hall PTR.

[22]

National Instruments LabVIEW. [n.d.]. https://www.ni.com/en-us/shop/labview/labview-details.html.

[23]

Miguel Angel Lagunas, M Eugenia Santamaria, Antoni Gasull, and A Moreno. 1986. Maximum likelihood filters in spectral estimation problems. Signal Processing 10, 1 (1986), 19--34.

Digital Library

[24]

Xianming Lang, Ping Li, Jiangtao Cao, Yan Li, and Hong Ren. 2018. A Small Leak Localization Method for Oil Pipelines Based on Information Fusion. IEEE Sensors Journal 18, 15 (2018), 6115--6122.

[25]

Wei Liang, Laibin Zhang, Qingqing Xu, and Chunying Yan. 2013. Gas pipeline leakage detection based on acoustic technology. Engineering Failure Analysis 31 (2013), 1--7.

[26]

Cui-wei Liu, Yu-xing Li, Yu-kun Yan, Jun-tao Fu, and Yu-qian Zhang. 2015. A new leak location method based on leakage acoustic waves for oil and gas pipelines. Journal of Loss Prevention in the Process Industries 35 (2015), 236--246.

[27]

Dimitris G Manolakis, Vinay K Ingle, Stephen M Kogon, et al. 2000. Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing. McGraw-Hill Boston.

[28]

S Lawrence Marple Jr and William M Carey. 1989. Digital spectral analysis with applications.

[29]

Mathworks MatLab. [n.d.]. https://www.mathworks.com/products/matlab.html.

[30]

Lingya Meng, Li Yuxing, Wang Wuchang, and Fu Juntao. 2012. Experimental study on leak detection and location for gas pipeline based on acoustic method. Journal of Loss Prevention in the Process Industries 25, 1 (2012), 90--102.

[31]

National Instruments NI-9232 module. [n.d.]. http://www.ni.com/en-us/support/model.ni-9232.html.

[32]

A Mostafapour and S Davoodi. 2015. A theoretical and experimental study on acoustic signals caused by leakage in buried gas-filled pipe. Applied Acoustics 87 (2015), 1--8.

[33]

Pal-Stefan Murvay and Ioan Silea. 2012. A survey on gas leak detection and localization techniques. Journal of Loss Prevention in the Process Industries 25, 6 (2012), 966--973.

[34]

B Musicus. 1985. Fast MLM power spectrum estimation from uniformly spaced correlations. IEEE Transactions on Acoustics, Speech, and Signal Processing 33, 5 (1985), 1333--1335.

[35]

Abdulfattah M Obeid, Fatma Karray, Mohamed Wassim Jmal, Mohamed Abid, Syed Manzoor Qasim, and Mohammed S BenSaleh. 2016. Towards realisation of wireless sensor network-based water pipeline monitoring systems: a comprehensive review of techniques and platforms. IET science, measurement & technology 10, 5 (2016), 420--426.

[36]

Georgios A Papadakis. 1999. Major hazard pipelines: a comparative study of onshore transmission accidents. Journal of Loss Prevention in the Process Industries 12, 1 (1999), 91--107.

[37]

Peyton Z Peebles. 2001. Probability, random variables, and random signal principles. Vol. 3. McGraw-Hill New York.

[38]

Hellenic Petroleum S.A. [n.d.]. https://www.helpe.gr/en/.

[39]

Ali Sadeghioon, Nicole Metje, David Chapman, and Carl Anthony. 2014. Smart-Pipes: smart wireless sensor networks for leak detection in water pipelines. Journal of sensor and Actuator Networks 3, 1 (2014), 64--78.

[40]

Ivan Stoianov, Lama Nachman, Sam Madden, and Timur Tokmouline. 2007. PIPENETa wireless sensor network for pipeline monitoring. In Proceedings of the 6th international conference on Information processing in sensor networks. ACM, 264--273.

Digital Library

[41]

Petre Stoica, Andreas Jakobsson, and Jian Li. 1998. Matched-filter bank interpretation of some spectral estimators. Signal Processing 66, 1 (1998), 45--59.

Digital Library

[42]

Petre Stoica, Randolph L Moses, et al. 2005. Spectral analysis of signals. (2005).

[43]

Sergios Theodoridis. 2015. Machine learning: a Bayesian and optimization perspective. Academic Press.

[44]

Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, and Dionisis Cavouras. 2010. Introduction to pattern recognition: a matlab approach. Academic Press.

[45]

Saeed V Vaseghi. 2008. Advanced digital signal processing and noise reduction. John Wiley & Sons.

[46]

Fang Wang, Weiguo Lin, Zheng Liu, Shuochen Wu, and Xiaobo Qiu. 2017. Pipeline leak detection by using time-domain statistical features. IEEE Sensors Journal 17, 19 (2017), 6431--6442.

[47]

Peter Welch. 1967. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics 15, 2 (1967), 70--73.

[48]

Gordon M Wenz. 1962. Acoustic ambient noise in the ocean: Spectra and sources. The Journal of the Acoustical Society of America 34, 12 (1962), 1936--1956.

[49]

Matthias Wolfel and John McDonough. 2005. Minimum variance distortionless response spectral estimation. IEEE Signal Processing Magazine 22, 5 (2005), 117--126.

[50]

Rui Xiao, Qunfang Hu, and Jie Li. 2019. Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine. Measurement 146 (2019), 479--489.

[51]

Morteza Zadkarami, Mehdi Shahbazian, and Karim Salahshoor. 2017. Pipeline leak diagnosis based on wavelet and statistical features using Dempster-Shafer classifier fusion technique. Process safety and environmental protection 105 (2017), 156--163.

[52]

Mengfei Zhou, Zheng Pan, Yunwen Liu, Qiang Zhang, Yijun Cai, and Haitian Pan. 2019. Leak Detection and Location Based on ISLMD and CNN in a Pipeline. IEEE Access 7 (2019), 30457--30464.

Cited By

Naranjo JCaiza GVelastegui RCastro MAlarcon-Ortiz AGarcia M(2022)A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0Sustainability10.3390/su14241672314:24(16723)Online publication date: 13-Dec-2022
https://doi.org/10.3390/su142416723
Glentis GGeorgoulaki KAngelopoulos K(2021)Efficient selection of time domain features for leakage detection in pipes carrying liquid commodities2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC50364.2021.9459811(1-6)Online publication date: 17-May-2021
https://dl.acm.org/doi/10.1109/I2MTC50364.2021.9459811

Index Terms

Study of the acoustic noise in pipelines carrying oil products in a refinery establishment
1. Hardware
  1. Communication hardware, interfaces and storage

Recommendations

Leak detection in the pipeline network of an oil refinery - A pattern classification paradigm
PCI '20: Proceedings of the 24th Pan-Hellenic Conference on Informatics

Leak detection and localization based on acoustic leak noise has been the subject of extensive research and a number of methods have been proposed for this purpose. It is a conceptually simple, non intrusive and easy to deploy approach for monitoring ...
Treatment of Alkaline Wastewater from Oil Refinery Using Circulating Biological Aerated Filter
CDCIEM '11: Proceedings of the 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring

The alkaline wastewater from oil refinery is a high concentration alkaline wastewater, which contains a great deal of sulfide phenol and oil pollutants. A novel two stage circulating biological aerated filter (CBAF) was used to treat the alkaline ...
Study on Pollution Mechanism of Petroleum Contamination in a Groundwater System under a Refinery in Jinan City
ESIAT '09: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02

The petroleum hydrocarbon contaminant from oil industry can enter the soil and badly influence local environment. We take a groundwater system polluted by refinery’s discharged oil as research object in this paper. Firstly, we collect soil and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

PCI '19: Proceedings of the 23rd Pan-Hellenic Conference on Informatics

November 2019

165 pages

ISBN:9781450372923

DOI:10.1145/3368640

General Chairs:
Yannis Manolopoulos
Open University Cyprus, Cyprus
,
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
Athena Stassopoulou
University of Nicosia, Cyprus
,
Program Chairs:
Ioanna Dionysiou
University of Nicosia, Cyprus
,
Ioannis Kyriakides
University of Nicosia, Cyprus
,
Nicolas Tsapatsoulis
Cyprus University of Technology, Cyprus

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

PCI '19

PCI '19: 23rd Pan-Hellenic Conference on Informatics

November 28 - 30, 2019

Nicosia, Cyprus

Acceptance Rates

PCI '19 Paper Acceptance Rate 18 of 35 submissions, 51%;

Overall Acceptance Rate 190 of 390 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
63
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Naranjo JCaiza GVelastegui RCastro MAlarcon-Ortiz AGarcia M(2022)A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0Sustainability10.3390/su14241672314:24(16723)Online publication date: 13-Dec-2022
https://doi.org/10.3390/su142416723
Glentis GGeorgoulaki KAngelopoulos K(2021)Efficient selection of time domain features for leakage detection in pipes carrying liquid commodities2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC50364.2021.9459811(1-6)Online publication date: 17-May-2021
https://dl.acm.org/doi/10.1109/I2MTC50364.2021.9459811

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents