Time series classification for predictive maintenance on event logs

Antoine Guillaume, Christel Vrain, Elloumi Wael

Time series classification (TSC) gained a lot of attention in the pastdecade and number of methods for representing and classifying time series havebeen proposed. Nowadays, methods based on convolutional networks and ensembletechniques represent the state of the art for time series classification. Techniquestransforming time series to image or text also provide reliable ways to extractmeaningful features or representations of time series. We compare the state-of-the-art representation and classification methods on a specific application, thatis predictive maintenance from sequences of event logs. The contributions of thispaper are twofold: introducing a new data set for predictive maintenance on auto-mated teller machines (ATMs) log data and comparing the performance of differentrepresentation methods for predicting the occurrence of a breakdown. The prob-lem is difficult since unlike the classic case of predictive maintenance via signalsfrom sensors, we have sequences of discrete event logs occurring at any time andthe lengths of the sequences, corresponding to life cycles, vary a lot.

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