TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Fault detection of elevator systems using deep autoencoder feature extraction

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoIEEE 13th International Conference on Research Challenges in Information Science
AlaotsikkoRCIS 2019, 29-31 May 2019, Brussels, Belgium
ToimittajatSamedi Heng
JulkaisupaikkaBrussels, Belgium
KustantajaIEEE
Sivut43-48
Sivumäärä6
Vuosikerta13
Painos2019
DOI - pysyväislinkit
TilaJulkaistu - 31 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE
Ajanjakso1/01/00 → …

Tiivistelmä

In this research, we propose a generic deep autoencoder model for automated feature extraction from the raw sensor data. 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 is 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 outperform the results using existing features. Existing features are also classified with random forest to compare results. Deep autoencoder random forest provides better results due to the new deep features extracted from the dataset when compared to existing features. Our model provides good classification and is robust against overfitting characteristics. This research will help various predictive maintenance systems to detect false alarms, which will reduce unnecessary visits of service technicians to installation sites.

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