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EnglishPredictive 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.
| Titolo: | Mining Sensor Data for Predictive Maintenance in the Automotive Industry |
| Autori: | |
| Data di pubblicazione: | 2018 |
| Abstract: | Predictive maintenance is an ever-growing area of interest, spanning different fields and approac...hes. 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. |
| ISBN: | 978-1-5386-5090-5 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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|---|---|---|---|---|
| 08631484.pdf | versione finale | 2. Post-print | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2725361
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