A Constraint-Based Approach to Predictive Maintenance Model Development
AffiliationCharles River Analytics
constraint-based data mining
domain aggregate approximation
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RightsCopyright © held by the author; distribution rights International Foundation for Telemetering
Collection InformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
AbstractPredictive maintenance is the combination of inspection and data analysis to perform maintenance when the need is indicated by unit performance. Significant cost savings are possible while preserving a high level of system performance and readiness. Identifying predictors of maintenance conditions requires expert knowledge and the ability to process large data sets. This paper describes a novel use of constraint-based data-mining to model exceedence conditions. The approach extends the extract, transformation, and load process with domain aggregate approximation to encode expert knowledge. A data-mining workbench enables an expert to pose hypotheses that constrain a multivariate data-mining process.
SponsorsInternational Foundation for Telemetering