Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

Gian Antonio Susto, Andrea Schirru, Simone Pampuri, Seán McLoone, Alessandro Beghi

Research output: Contribution to journalArticle

553 Citations (Scopus)
19871 Downloads (Pure)


In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.
Original languageEnglish
Pages (from-to)812-820
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number3
Early online date18 Aug 2014
Publication statusPublished - Jun 2015


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