Water distribution systems, and other infrastructures, are increasingly being pervaded by sensing technologies, collecting a growing volume of data aimed at supporting operational and investment decisions. These sensors monitor system characteristics, i.e. flows, pressures and water quality, such as in pipes. This paper presents the application of pattern matching techniques and binary associative neural networks for novelty detection in such data. A protocol for applying pattern matching to automatically recognise specific waveforms in time series based on their shapes is described together with a system called Advanced Uncertain Reasoning Architecture (AURA) Alert for autonomous determination of novelty. AURA is a class of binary neural network that has a number of advantages over standard artificial neural network techniques for condition monitoring including a sound theoretical basis to determine the bounds of the system operation. Results from application to several case studies are provided including both hydraulic and water quality data. In the case of pattern matching, the results demonstrated some transferability of burst patterns across District Metered Areas; however limitations in performance and difficulties with assembling pattern libraries were found. Results for the AURA system demonstrate the potential for robust event detection across multiple parameters providing valuable information for diagnosis; one example also demonstrates the potential for detection of precursor information, vital for proactive management.
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Research Article|
October 08 2013
Pattern matching and associative artificial neural networks for water distribution system time series data analysis
S. R. Mounce;
1Pennine Water Group, Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S1 3JD, UK
E-mail: s.r.mounce@sheffield.ac.uk
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R. B. Mounce;
R. B. Mounce
1Pennine Water Group, Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S1 3JD, UK
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T. Jackson;
T. Jackson
2Advanced Computer Architecture Group, Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK
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J. Austin;
J. Austin
2Advanced Computer Architecture Group, Department of Computer Science, University of York, Deramore Lane, York, YO10 5GH, UK
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J. B. Boxall
J. B. Boxall
1Pennine Water Group, Department of Civil and Structural Engineering, University of Sheffield, Sheffield, S1 3JD, UK
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Journal of Hydroinformatics (2014) 16 (3): 617–632.
Article history
Received:
May 13 2013
Accepted:
August 13 2013
Citation
S. R. Mounce, R. B. Mounce, T. Jackson, J. Austin, J. B. Boxall; Pattern matching and associative artificial neural networks for water distribution system time series data analysis. Journal of Hydroinformatics 1 May 2014; 16 (3): 617–632. doi: https://doi.org/10.2166/hydro.2013.057
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