Tampere University of Technology

TUTCRIS Research Portal

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

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

Details

Original languageEnglish
Title of host publication11th International Conference on Knowledge Engineering and Ontology Development
Subtitle of host publicationKEOD 2019, September 17 - 19, 2019 in Vienna, Austria
EditorsJoaquim Filipe, Jan Dietz, David Aveiro
Place of PublicationPortugal
PublisherSCITEPRESS
Pages336-342
Number of pages7
Volume2
Edition2019
ISBN (Electronic)978-989-758-382-7
DOIs
Publication statusPublished - 19 Sep 2019
Publication typeA4 Article in a conference publication
EventINTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT -
Duration: 1 Jan 1900 → …

Publication series

NameInternational Conference on Knowledge Engineering and Ontology Development
PublisherSCITEPRESS
ISSN (Print)2184-3228
ISSN (Electronic)2184-3228

Conference

ConferenceINTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT
Period1/01/00 → …

Abstract

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.

ASJC Scopus subject areas

Publication forum classification

Field of science, Statistics Finland