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Journal of Intelligent Manufacturing

Erschienen in:

27.09.2019

On mining frequent chronicles for machine failure prediction

verfasst von: Chayma Sellami, Carlos Miranda, Ahmed Samet, Mohamed Anis Bach Tobji, François de Beuvron

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2020

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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.

Vorheriger Artikel Shop floor data-driven spatial–temporal verification for manual assembly planning

Nächster Artikel Conductive particle detection via deep learning for ACF bonding in TFT-LCD manufacturing

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In this paper, we mean by “breakdown” a failure.

In this paper, we mean by “instant” a given unit of time.

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Titel: On mining frequent chronicles for machine failure prediction
verfasst von: Chayma Sellami
Carlos Miranda
Ahmed Samet
Mohamed Anis Bach Tobji
François de Beuvron
Publikationsdatum: 27.09.2019
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2020
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-019-01492-x

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