Frequent Chronicle Mining: Application on Predictive Maintenance

Abstract

Chronicles are a kind of sequential patterns that consider the time dimension to produce relevant knowledge for decision makers. Mined from pairs of event-time, chronicles are represented in graphs for which vertices are events and edges are labeled with intervals representing the time between the two linked events. Chronicle mining is interesting in several domains where predicting the time interval of an event is important, such as network failure analysis, pharmaco-epidemiology and human activities analysis. In this work, we are interested in predicting the failure time of monitored industrial machines. We introduce a new approach to mine the most relevant chronicles in an industrial data set. The extracted chronicles are then used to predict the failure time of a given machine. Our approach is validated through several experiments led on a benchmark data set.