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

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


Original languageEnglish
Title of host publication23rd International Conference Information Visualisation
Subtitle of host publicationIV 2019, 2-5 July 2019, Paris, France
EditorsFatma Bouali
Place of PublicationParis, France
Number of pages6
ISBN (Electronic)978-1-7281-2838-2
ISBN (Print)978-1-7281-2839-9
Publication statusPublished - 5 Jul 2019
Publication typeA4 Article in a conference publication
EventInternational Conference Information Visualisation -
Duration: 6 Aug 2019 → …

Publication series

NameProceedings : International Conference on Information Visualisation
ISSN (Print)1550-6037
ISSN (Electronic)2375-0138


ConferenceInternational Conference Information Visualisation
Period6/08/19 → …


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 including both acceleration and magnetic signals. In addition, 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 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

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