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Fault detection of elevator system using profile extraction and deep autoencoder feature extraction

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication33rd Annual European Simulation and Modelling Conference
Subtitle of host publicationESM 2019, October 28-30, 2019, Palma de Mallorca, Spain
EditorsPhilippe Geril
Place of PublicationBelgium
PublisherEUROSIS
Pages79-83
Number of pages5
Volume33
Edition2019
ISBN (Print)9789492859099
Publication statusPublished - 30 Oct 2019
Publication typeA4 Article in a conference publication
EventEuropean Simulation and Modelling Conference -
Duration: 1 Jan 1900 → …

Publication series

NameEuropean Simulation and Modelling Conference
PublisherEUROSIS

Conference

ConferenceEuropean Simulation and Modelling Conference
Period1/01/00 → …

Abstract

In this paper, we propose a new algorithm for data extraction from time series 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 rest of the healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved nearly 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 when 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.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland