On mining frequent chronicles for machine failure prediction

Chayma Sellami (), Carlos Miranda, Ahmed Samet, Mohamed Anis Bach Tobji and François de Beuvron
Additional contact information
Chayma Sellami: Univ. Manouba
Carlos Miranda: INSA Rouen
Ahmed Samet: ICUBE / SDC Team (UMR CNRS 7357)
Mohamed Anis Bach Tobji: Université de Tunis
François de Beuvron: ICUBE / SDC Team (UMR CNRS 7357)

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 14, 1019-1035

Abstract: Abstract In industry 4.0, machines generate a lot of data about several kinds of events that occur in the production process. This huge quantity of information contains valuable patterns that allow prediction of important events in the appropriate instant. In this paper, we are interested in mining frequent chronicles in the context of industrial data. We introduce a general approach to preprocess, mine, and use frequent chronicles to predict a special event; the failure of a machine. Our approach aims not only to predict the failure, but also the time of its appearance. Our approach is validated through a set of experiments performed on the chronicle mining phase as well as the prediction phase. Experiments were achieved on synthetic data in addition to a real industrial data set.

Keywords: Chronicle mining; Predictive maintenance; Industry 4.0 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-019-01492-x

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