Domain Knowledge in Predictive Maintenance for Water Pipe Failures

Li, Z; Wang, Y

Domain Knowledge in Predictive Maintenance for Water Pipe Failures

Li, Z

Wang, Y

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Human and Machine Learning, 2018, pp. 437-457
Issue Date:: 2018-06-08

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Field	Value	Language
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X
dc.contributor.author	Wang, Y
dc.date.accessioned	2022-12-22T05:28:17Z
dc.date.available	2022-12-22T05:28:17Z
dc.date.issued	2018-06-08
dc.identifier.citation	Human and Machine Learning, 2018, pp. 437-457
dc.identifier.isbn	978-3-319-90402-3
dc.identifier.uri	http://hdl.handle.net/10453/164518
dc.description.abstract	In this chapter, the water pipe failure prediction is used as an example to show the integration of machine learning and domain knowledge. It is crucial for the risk management strategy of water distribution systems to minimise the water pipe failure impacts. Prediction of water pipe conditions through statistical modelling is an important element for the task. When applying the models to practical problems, domain experts can provide invaluable suggestions that can be used as constraints or informative prior knowledge. Alternatively, the models can also help domain experts to explore more insights. The chapter uses major steps in the water pipe failure prediction, including data review, factor analysis, prediction evaluation and practical use, as examples to illustrate how the domain knowledge is integrated. Then the hierarchical non-parametric model is used as an example model
dc.format.extent	22
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Human and Machine Learning
dc.relation.ispartofseries	Human–Computer Interaction Series
dc.relation.isbasedon	10.1007/978-3-319-90403-0_21
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Domain Knowledge in Predictive Maintenance for Water Pipe Failures
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-22T05:28:15Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
dc.location	Switzerland

Abstract:

In this chapter, the water pipe failure prediction is used as an example to show the integration of machine learning and domain knowledge. It is crucial for the risk management strategy of water distribution systems to minimise the water pipe failure impacts. Prediction of water pipe conditions through statistical modelling is an important element for the task. When applying the models to practical problems, domain experts can provide invaluable suggestions that can be used as constraints or informative prior knowledge. Alternatively, the models can also help domain experts to explore more insights. The chapter uses major steps in the water pipe failure prediction, including data review, factor analysis, prediction evaluation and practical use, as examples to illustrate how the domain knowledge is integrated. Then the hierarchical non-parametric model is used as an example model

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http://hdl.handle.net/10453/164518