TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Fault detection of elevator system using deep autoencoder feature extraction for acceleration signals

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko11th International Conference on Knowledge Engineering and Ontology Development
AlaotsikkoKEOD 2019, September 17 - 19, 2019 in Vienna, Austria
ToimittajatJoaquim Filipe, Jan Dietz, David Aveiro
JulkaisupaikkaPortugal
KustantajaSCITEPRESS
Sivut336-342
Sivumäärä7
Vuosikerta2
Painos2019
ISBN (elektroninen)978-989-758-382-7
DOI - pysyväislinkit
TilaJulkaistu - 19 syyskuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiInternational Conference on Knowledge Engineering and Ontology Development
KustantajaSCITEPRESS
ISSN (painettu)2184-3228
ISSN (elektroninen)2184-3228

Conference

ConferenceINTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT
Ajanjakso1/01/00 → …

Tiivistelmä

In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator time series data. Random forest algorithm is used for fault detection based on extracted deep features. Maintenance actions recorded are used to label the sensor data into healthy or faulty. Avoiding false positives are performed with the rest of the healthy data in terms of validation of the model to prove its efficacy. New extracted deep features provide 100% accuracy in fault detection along with avoiding false positives, which is better than existing features. Random forest was also used to detect faults based on existing features to compare results. New deep features extracted from the dataset with deep autoencoder random forest outperform the existing features. Good classification and robustness against overfitting are key characteristics of our model. This research will help to reduce unnecessary visits of service technicians to installation sites by detecting false alarms in various predictive maintenance systems.

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