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TUTCRIS

Profile extraction and deep autoencoder feature extraction for elevator fault detection

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko16th International Conference on Signal Processing and Multimedia Applications
AlaotsikkoSIGMAP 2019, 26-28 July, 2019, Prague, Czech Republic
ToimittajatChristian Callegari
JulkaisupaikkaPrague, Czech Republic
KustantajaSCITEPRESS
Sivut313-320
Sivumäärä8
Vuosikerta16
Painos2019
ISBN (elektroninen)978-989-758-378-0
ISBN (painettu)978-989-758-378-0
DOI - pysyväislinkit
TilaJulkaistu - 28 heinäkuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceINTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS
Ajanjakso1/01/00 → …

Tiivistelmä

In this paper, we propose a new algorithm for data extraction from time series signal data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction elevator start and stop events are extracted from sensor data, and a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved 100% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.

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