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Fault detection of elevator systems using automated feature extraction and classification

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Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoElevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies
Alaotsikko22-24 May 2018, Berlin, Germany.
JulkaisupaikkaBerlin
KustantajaThe International Association of Elevator Engineers
Sivut116-122
Sivumäärä7
Vuosikerta22
Painos2018
ISBN (painettu)978-965-572-261-1
TilaJulkaistu - 21 toukokuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Congress on Vertical Transportation Technologies -
Kesto: 27 kesäkuuta 2018 → …

Conference

ConferenceInternational Congress on Vertical Transportation Technologies
Ajanjakso27/06/18 → …

Tiivistelmä

In this research, we study an automated feature extraction technique to calculate new features from raw sensor data provided by an elevator data recording system and to create a more generic machine learning model for fault detection. Another data set called maintenance data is used to find the time period for creating class variables. The calculated features attached to
class variables are classified as healthy or faulty using random forest algorithm. The time period starts from a fault reported by the customer and ends when maintenance is finished and reported. We use accuracy, sensitivity and specificity as evaluation parameters for this research.

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