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.

中文翻译：

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关于挖掘频繁编年史以预测机器故障

摘要

在工业4.0中，机器会生成有关在生产过程中发生的几种事件的大量数据。大量信息包含有价值的模式，这些模式允许在适当的时刻预测重要事件。在本文中，我们对在工业数据的背景下挖掘频繁编年史感兴趣。我们介绍了一种通用方法来进行预处理，挖掘和使用频繁的编年史来预测特殊事件。一台机器的故障。我们的方法不仅旨在预测故障，而且还旨在预测其出现的时间。我们的方法已通过在编年史挖掘阶段和预测阶段进行的一组实验验证。除了实际的工业数据集之外，还对合成数据进行了实验。