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

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Krishna Mohan Mishra, John-Eric Saxen, Jerker Björkqvist, Kalevi Huhtala
Publiceringsår: 2019
Moderpublikationens namn: 33rd Annual European Simulation and Modelling Conference
Volym: 33
Artikelns första sida, sidnummer: 79
Artikelns sista sida, sidnummer: 83
ISBN: 9789492859099


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.


Senast uppdaterad 2020-08-04 vid 08:05