Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Modeling and Monitoring of Pipelines and Networks

Part of the book series: Applied Condition Monitoring ((ACM,volume 7))

Abstract

This chapter serves as a background of monitoring systems for pipeline networks by applying software-based fault diagnosis tools and briefly describes each contribution of this monograph. The term computational pipeline monitoring (CPM) refers to algorithmic monitoring tools that are used to enhance the abilities of the pipeline network’s operators in recognizing anomalies which may be indicative of products’ loss. The presented advanced methods and issues associated with models and features of pipeline networks are limited to scenarios of leaks and blockages in a single pipeline and pipeline networks. The framework of the procedures considers the physical variables associated with the flow process as measurements. In particular, pressure, flow, and temperature sensors are assumed to be located only at specific points of the pipeline networks. Thus, this chapter introduces the reader to the main theme of the monograph which is the analysis and design of advanced online automatic monitoring systems for pipeline networks by considering leaks and blocks as abnormal events. To simplify the understanding of the specific topics, the general fault detection and isolation (FDI) background is roughly presented in this chapter by citing tutorial books related to the FDI issues.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 3rd SysTol (Ed.). (2016). In 2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol).

    Google Scholar 

  • 8th IFAC-Safeprocess (Ed.). (2012). In IFAC symposium on fault detection, supervision and safety for technical processes (Safeprocess).

    Google Scholar 

  • 9th IFAC-Safeproocess (Ed.). (2015). In IFAC symposium on fault detection, supervision and safety for technical processes (Safeprocess).

    Google Scholar 

  • American Petroleum Institute (1995). API-1130 computational pipeline monitoring.

    Google Scholar 

  • Arsene, C. T. C., Gabrys, B., & Al-dabass, D. (2012). Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39(18), 13214–13224.

    Article  Google Scholar 

  • Billman, L., & Isermann, R. (1987). Leak detection methods for pipelines. Automatica, 23(3), 381–385.

    Article  MATH  Google Scholar 

  • Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki, M. (2006). Diagnosis and fault tolerant control (2nd ed.). Berlin: Springer.

    MATH  Google Scholar 

  • Brunone, B., & Ferrante, M. (2001). Detecting leaks in pressurised pipes by means of transients. Journal of Hydraulic Research, 39(5), 539–547.

    Article  Google Scholar 

  • Brunone, B. (1999). Transient test-based technique for leak detection in outfall pipes. Journal of Water Resources Planning and Management ASCE, 125–5, 302–306.

    Article  Google Scholar 

  • Casillas, M. V., Garza-Castañón, L. E., & Puig, V. (2015). Sensor placement for leak location in water distribution networks using the leak signature space. 9th IFAC symposium on fault detection, supervision and safety of technical processes (pp. 214–219). Paris, France: IFAC.

    Google Scholar 

  • Chaudhry, H. M. (2014). Applied hydraulic transients. New York: Springer.

    Book  Google Scholar 

  • Colombo, A. F., & Karney, B. W. (2002). Energy and costs of leaky pipes: Toward comprehensive picture. Journal of Water Resources Planning and Management (ASCE), 128, 441–450.

    Article  Google Scholar 

  • Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-Environment Research, 2, 212–227.

    Article  Google Scholar 

  • Da Silva, H. V., Morooka, C. K., Guilherme, I. R., da Fonseca, T. C., & Mendes, J. R. P. (2005). Leak detection in petroleum pipelines using fuzzy system. Journal of Petroleum Science and Engineering, 49, 223–238.

    Google Scholar 

  • David Steffelbauer, B., & Fuchs-Hanusch, D. (2016). Efficient Sensor Placement for Leak Localization Considering Uncertainties. Water Resources Management, 30(14), 5517–5533.

    Google Scholar 

  • Delgado-Aguiñaga, J., Besançon, G., Begovich, O., & Carvajal, J. E. (2016). Multi-leak diagnosis in pipelines based on extended kalman filter. Control Engineering Practice, 49, 139–148.

    Article  Google Scholar 

  • Dinis, J. M., Wojtanowicz, A. K., & Scott, S. L. (1999). Leak detection in liquid subsea flowlines with no recorded feed rate. Journal of Energy Resources Technology by ASME, 121, 161–166.

    Article  Google Scholar 

  • EPA (2009). Drinking water infrastructure needs survey and assessment. Technical Report, U.S. Environmental Protection Agency (EPA).

    Google Scholar 

  • Ferrante, M., & Brunone, B. (2003a). Pipe system diagnosis and leak detection by usteady-state tests. 1. Harmonic Analysis. Advances in Water Resources, 26(1), 95–105.

    Article  Google Scholar 

  • Ferrante, M., & Brunone, B. (2003b). Pipe system diagnosis and leak detection by unsteady-state tests. 2. Wavelet analysis Advances in Water Resources, 26, 107–116.

    Article  Google Scholar 

  • Geiger, G., Gregoritza, W., & Matko, D. (2000). Leak detection and localisation in pipes and pipelines. In European symposium on computer aided process engineering-10 (Vol. 2, pp. 781–786).

    Google Scholar 

  • Gertler, J., Romera, J., Puig, V., & Quevedo, J. (2010). Leak detection and isolation in water distribution networks using principal component analysis and structured residuals. In Conference on Control and Fault Tolerant Systems (pp. 1–6). Nice, France: IEEE.

    Google Scholar 

  • Isermann, R. (2006). Fault diagnosis system. Heidelberg: Springer.

    Book  Google Scholar 

  • Knudsen, O. Ø. (2013). Pipelines carry out their own health checks. Technical Report, SINTEF. http://www.sintef.no/en/latest-news/pipelines-carry-out-their-own-health-checks/.

  • Lee, P., Vitkovsky, J., Lambert, M., Simpson, A., & Liggett, J. (2005). Leak location using the pattern of the frequency response diagram in pipelines: A numerical study. Journal of Sound and Vibration, 284, 1051–1075.

    Article  Google Scholar 

  • Mounce, S. R., Mounce, R. B., & Boxall, J. B. (2011). Novelty detection for time series data analysis in water distribution systems using support vector machines. Journal of Hydroinformatics, 13(4), 672–686.

    Article  Google Scholar 

  • Palau, C., Arregui, F., & Carlos, M. (2012). Burst detection in water networks using principal component analysis. Journal of Water Resources Planning and Management, 138(1), 47–54.

    Article  Google Scholar 

  • Patton, R. J., Frank, P. M., & Clark, R. N. (1989). Fault diagnosis in dynamic systems: Theory and applications. New York: Prentice Hall.

    Google Scholar 

  • Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19, 1157–1167. doi:10.1016/j.conengprac.2011.06.004.

  • Pipeline Studio (2013). Software. In Energy solutions international. http://www.energy-solutions.com/.

  • Pudar, B. R. S., Member, A., & Liggett, J. A. (1992). Leaks in pipe networks. Journal of Hydraulic Engineering, 118(7), 1031–1046.

    Article  Google Scholar 

  • Shields, D., & Daley, S. (2001). Design of nonlinear observers for detecting faults in hydraulic subsea pipelines. Control Engineering Practice, 9, 297–311.

    Google Scholar 

  • Venkatasubramanian, V., Rengaswamyd, R., Yin, R., & Kavuri, S. (2003a). A review of process fault detection and diagnosis: Part i: Quantitative model based methods. Computers and Chemical Engineering, 27, 293–311.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamyd, R., Yin, R., & Kavuri, S. (2003b). A review of process fault detection and diagnosis: Part ii: Qualitative model and search strategies. Computers and Chemical Engineering, 27, 313–326.

    Article  Google Scholar 

  • Venkatasubramanian, V., Rengaswamyd, R., Yin, R., & Kavuri, S. (2003c). A review of process fault detection and diagnosis: Part iii: Process history-based methods. Computers and Chemical Engineering, 27, 326–346.

    Article  Google Scholar 

  • Verde, C., Molina, L., & Torres, L. (2014). Parametrized transient model of a pipeline for multiple leaks location. Journal of Loss Prevention in the Process Industries, 29, 177–185.

    Article  Google Scholar 

  • Verde, C., Torres, L., & González, O. (2016). Decentralized scheme for leaks’ location in a branched pipeline. Journal of Loss Prevention in the Process Industries, 43, 18–28.

    Article  Google Scholar 

  • Verde, C., & Visairo, N. (2004). Identificability of multi-leaks in a pipeline. In Proceedings of the American Control Conference 2004. ISBN-0-7803-8336-2.

    Google Scholar 

  • Visairo, N., & Verde, C. (2003). Leak isolation conditions in a pipeline via a geometric approach. In 3rd IFAC-SAFEPROCESS symposium (pp. 1023–1028).

    Google Scholar 

  • Wu, Y., Liu, S., Wu, X., Liu, Y., & Guan, Y. (2016). Burst detection in district metering areas using a data driven clustering algorithm. Water Research, 100, 28–37.

    Article  Google Scholar 

  • Wylie, E. B., & Streeter, V. L. (1978). Fluid Transients. McGraw-Hill International Book Co.

    Google Scholar 

  • Ye, G., & Fenner, R. A. (2014). Weighted Least Squares with Expectation Maximization Algorithm for Burst Detection in U. K. Water Distribution Systems. Journal of Water Resources Planning and Management, 140(4), 417–424. doi:10.1061/(ASCE)WR.1943-5452.0000344.

  • Zhang, J. (1996). Designing a cost effective and reliable pipeline leak detection system. In Pipeline Reliability Conference, Houston, USA, November 19-22.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto de Ingeniería UNAM, CP 04510, Mexico City, Mexico

    Cristina Verde

Authors
  1. Cristina Verde
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Verde .

Editor information

Editors and Affiliations

  1. Instituto de Ingeniería-UNAM, Ciudad Universitaria, Coyoacán, Mexico

    Cristina Verde

  2. Instituto de Ingeniería-UNAM, Ciudad Universitaria, Coyoacán, Mexico

    Lizeth Torres

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Verde, C. (2017). Introduction. In: Verde, C., Torres, L. (eds) Modeling and Monitoring of Pipelines and Networks. Applied Condition Monitoring, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-55944-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55944-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55943-8

  • Online ISBN: 978-3-319-55944-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation