Predictive maintenance is an ever-growing area of interest, spanning different fields and approaches. In the automotive industry faulty behaviors of the oxygen sensor are a key challenge to address. This paper presents OxyClog, a data-driven framework that, given a large number of time series collected from a vehicle's ECU (engine control unit), builds a model to predict if the oxygen sensor is currently unclogged, almost clogged (since the clogging of the sensor happens gradually), or clogged. OxyClog is characterized by a tailored preprocessing, which includes a custom and interpretable feature selection algorithm, along with a summarization strategy to transform a time-dependent problem into a time-independent one. Furthermore, a semi-supervised labeling methodology has been devised to use different data sources with different characteristics to define meaningful clogging labels. OxyClog integrates state-of-the-art classification algorithms – both interpretable and non-interpretable – to process real ECU data with good prediction performance.

Mining Sensor Data for Predictive Maintenance in the Automotive Industry / Giobergia, Flavio; Baralis, Elena; Camuglia, Maria; Cerquitelli, Tania; Mellia, Marco; Neri, Alessandra; Tricarico, Davide; Tuninetti, Alessia. - ELETTRONICO. - (2018), pp. 351-360. ((Intervento presentato al convegno IEEE International Conference on Data Science and Advanced Analytics (DSAA) tenutosi a Torino nel October 2018 [10.1109/DSAA.2018.00046].

Mining Sensor Data for Predictive Maintenance in the Automotive Industry

GIOBERGIA, FLAVIO;Baralis, Elena;Cerquitelli, Tania;Mellia, Marco;Neri, Alessandra;
2018

Abstract

Predictive maintenance is an ever-growing area of interest, spanning different fields and approaches. In the automotive industry faulty behaviors of the oxygen sensor are a key challenge to address. This paper presents OxyClog, a data-driven framework that, given a large number of time series collected from a vehicle's ECU (engine control unit), builds a model to predict if the oxygen sensor is currently unclogged, almost clogged (since the clogging of the sensor happens gradually), or clogged. OxyClog is characterized by a tailored preprocessing, which includes a custom and interpretable feature selection algorithm, along with a summarization strategy to transform a time-dependent problem into a time-independent one. Furthermore, a semi-supervised labeling methodology has been devised to use different data sources with different characteristics to define meaningful clogging labels. OxyClog integrates state-of-the-art classification algorithms – both interpretable and non-interpretable – to process real ECU data with good prediction performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2725361
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