Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system

Simple item page
dc.contributor.authorTang, Kayu
dc.contributor.authorParsons, David J.
dc.contributor.authorJude, Simon
dc.date.accessioned2019-04-05T08:42:09Z
dc.date.available2019-04-05T08:42:09Z
dc.date.issued2019-02-06
dc.description.abstractThe reliability of the water distribution system is critical to maintaining a secure supply for the population, industry and agriculture, so there is a need for proactive maintenance to help reduce water loss and down times. Bayesian networks are one approach to modelling the complexity of water mains, to assist water utility companies in planning maintenance. This paper compares and analyses how accurately the Bayesian network structure can be derived given a large and highly variable dataset. Method one involved using automated learning algorithms to build the Bayesian network, while method two involved a guided method using a combination of historic failure data, prior knowledge and pre-modelling data exploration of the water mains. By understanding common failure types (circumferential, longitudinal, pinhole and joint), the guided learning Bayesian Network was able to capture the interactions of the surrounding soil environment with the physical properties of pipes. The Bayesian network built using data exploration and literature was able to achieve an overall accuracy of 81.2% when predicting the specific type of water mains failure compared to the 84.4% for the automated method. The slightly greater accuracy from the automated method was traded for a sparser Bayes net where the interpretation of the interactions between the variables was clearer and more meaningful.en_UK
dc.identifier.citationKayu Tang, David J. Parsons and Simon Jude. Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system. Reliability Engineering and System Safety, Volume 186, June 2019, Pages 24-36en_UK
dc.identifier.issn0951-8320
dc.identifier.urihttps://doi.org/10.1016/j.ress.2019.02.001
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/14047
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBayesian networksen_UK
dc.subjectAsset managementen_UK
dc.subjectReliabilityen_UK
dc.subjectInfrastructureen_UK
dc.subjectStatisticsen_UK
dc.subjectWateren_UK
dc.titleComparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution systemen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bayesian_networks_to_analyse_pipe_failures-water.pdf
Size:
1.9 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)