skip to main content
10.1145/2821650.2821666acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article
Public Access

An Efficient Approach to Fault Identification in Urban Water Networks Using Multi-Level Sensing

Published:04 November 2015Publication History

ABSTRACT

The objective of this work is to develop an efficient and practical sensor placement method for the failure detection and localization in water networks. We formulate the problem as the minimum test cover problem (MTC) with the objective of selecting the minimum number of sensors required to uniquely identify and localize pipe failure events. First, we summarize a single-level sensing model and discuss an efficient fast greedy approach for solving the MTC problem. Simulation results on benchmark test networks demonstrate the efficacy of the fast greedy algorithm. Second, we develop a multi-level sensing model that captures additional physical features of the disturbance event, such as the time lapsed between the occurrence of disturbance and its detection by the sensor. Our sensor placement approach using MTC extends to the multi-level sensing model and an improved identification performance is obtained via reduced number of sensors (in comparison to single-level sensing model). In particular, we investigate the bi-level sensing model to illustrate the efficacy of employing multi-level sensors for the identification of failure events. Finally, we suggest extensions of our approach for the deployment of heterogeneous sensors in water networks by exploring the trade-off between cost and performance (measured in terms of the identification score of pipe/link failures).

References

  1. Bentley, Water Hammer and Transient Analysis Software. http://www.bentley.com/en-US/Products/HAMMER/. Accessed: 2015-04--14.Google ScholarGoogle Scholar
  2. J. Berry, W. Hart, C. Phillips, J. Uber, and J. Watson. Sensor placement in municipal water networks with temporal integer programming models. Journal of Water Resources Planning and Management, 132(4):218--224, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  3. K. M. De Bontridder, B. V. Halldórsson, M. M. Halldórsson, C. A. Hurkens, J. K. Lenstra, R. Ravi, and L. Stougie. Approximation algorithms for the test cover problem. Mathematical Programming, 98(1--3):477--491, 2003.Google ScholarGoogle Scholar
  4. A. Deshpande, S. E. Sarma, K. Youcef-Toumi, and S. Mekid. Optimal coverage of an infrastructure network using sensors with distance-decaying sensing quality. Automatica, 49(11):3351--3358, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Eliades and M. Polycarpou. A fault diagnosis and security framework for water systems. IEEE Transactions on Control Systems Technology, 18(6):1254--1265, Nov 2010.Google ScholarGoogle ScholarCross RefCross Ref
  6. B. V. Halldórsson, M. M. Halldórsson, and R. Ravi. On the approximability of the minimum test collection problem. In Proceedings of the 9th Annual European Symposium on Algorithms, ESA '01, pages 158--169, London, UK, UK, 2001. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W. Hart, J. Berry, E. Boman, R. Murray, C. Phillips, L. Riesen, and J. Watson. The TEVA-SPOT Toolkit for Drinking Water Contaminant Warning System Design, chapter 511, pages 1--12.Google ScholarGoogle Scholar
  8. W. Hart and R. Murray. Review of sensor placement strategies for contamination warning systems in drinking water distribution systems. Journal of Water Resources Planning and Management, 136(6), 2010.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. D. Jolly, A. D. Lothes, S. Bryson, and L. Ormsbee. Research database of water distribution system models. Journal of Water Resources Planning and Management, 140(4):410--416, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. T. Kim and S. J. Wright. PMU placement for line outage identification via multiclass logistic regression. arXiv:1409.3832v1 {math.OC}.Google ScholarGoogle Scholar
  11. A. Krause, J. Leskovec, C. Guestrin, J. Vanbriesen, and C. Faloutsos. Efficient sensor placement optimization for securing large water distribution networks. Journal of Water Resources Planning and Management, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  12. A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9:235--284, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Minoux. Accelerated greedy algorithms for maximizing submodular set functions. In Optimization Techniques, pages 234--243. Springer, 1978.Google ScholarGoogle ScholarCross RefCross Ref
  14. B. M. E. Moret and H. D. Shapiro. On minimizing a set of tests. SIAM Journal on Scientific and Statistical Computing, 6(4):983--1003, 1985.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Ostfeld, J. G. Uber, E. Salomons, J. W. Berry, W. E. Hart, Phillips, et al. The battle of the water sensor networks: A design challenge for engineers and algorithms. Journal of Water Resources Planning and Management, 134(6):556--568, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  16. R. Puust, Z. Kapelan, D. A. Savic, and T. Koppel. A review of methods for leakage management in pipe networks. Urban Water Journal, 7(1):25--45, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  17. L. Sela Perelman, W. Abbas, X. Koutsoukos, and S. Amin. Sensor placement for fault location identification in water networks: a minimum test cover approach. 2015. http://arxiv.org/abs/1507.07134 {cs.SY}.Google ScholarGoogle Scholar
  18. E. Wylie, V. Streeter, and L. Suo. Fluid transients in systems. Prentice Hall, 1993.Google ScholarGoogle Scholar
  19. T. T. Zan, H. B. Lim, K.-J. Wong, A. J. Whittle, and B.-S. Lee. Event detection and localization in urban water distribution network. IEEE Sensors Journal, 14(12):4134--4142, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  20. J. Zhang, F.-Y. Wang, K. Wang, W.-H. Lin, X. Xu, and C. Chen. Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4):1624--1639, Dec 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. An Efficient Approach to Fault Identification in Urban Water Networks Using Multi-Level Sensing

    Recommendations

    Comments

    Login options

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

    Sign in
    • Published in

      cover image ACM Conferences
      BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
      November 2015
      264 pages
      ISBN:9781450339810
      DOI:10.1145/2821650

      Copyright © 2015 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 the author(s) 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: 4 November 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      BuildSys '15 Paper Acceptance Rate20of66submissions,30%Overall Acceptance Rate148of500submissions,30%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader