Profile Extraction and Deep Autoencoder Feature Extraction for Elevator Fault Detection

Krishna Mishra, Tomi Krogerus, Kalevi Huhtala

Abstract

In this paper, we propose a new algorithm for data extraction from time series signal 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 remaining healthy data are 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 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 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.

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Paper Citation


in Harvard Style

Mishra K., Krogerus T. and Huhtala K. (2019). Profile Extraction and Deep Autoencoder Feature Extraction for Elevator Fault Detection.In Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP, ISBN 978-989-758-378-0, pages 313-320. DOI: 10.5220/0007802003130320


in Bibtex Style

@conference{sigmap19,
author={Krishna Mishra and Tomi Krogerus and Kalevi Huhtala},
title={Profile Extraction and Deep Autoencoder Feature Extraction for Elevator Fault Detection},
booktitle={Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP,},
year={2019},
pages={313-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007802003130320},
isbn={978-989-758-378-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on e-Business and Telecommunications - Volume 1: SIGMAP,
TI - Profile Extraction and Deep Autoencoder Feature Extraction for Elevator Fault Detection
SN - 978-989-758-378-0
AU - Mishra K.
AU - Krogerus T.
AU - Huhtala K.
PY - 2019
SP - 313
EP - 320
DO - 10.5220/0007802003130320