Fault Detection of Elevator System using Deep Autoencoder Feature Extraction for Acceleration Signals

Krishna Mohan Mishra, Kalevi J. Huhtala

2019

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.

Paper Citation

in Harvard Style

Mishra K. and Huhtala K. (2019). Fault Detection of Elevator System using Deep Autoencoder Feature Extraction for Acceleration Signals. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD; ISBN 978-989-758-382-7, SciTePress, pages 336-342. DOI: 10.5220/0008347403360342

in Bibtex Style

@conference{keod19,
author={Krishna Mohan Mishra and Kalevi J. Huhtala},
title={Fault Detection of Elevator System using Deep Autoencoder Feature Extraction for Acceleration Signals},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD},
year={2019},
pages={336-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008347403360342},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD
TI - Fault Detection of Elevator System using Deep Autoencoder Feature Extraction for Acceleration Signals
SN - 978-989-758-382-7
AU - Mishra K.
AU - Huhtala K.
PY - 2019
SP - 336
EP - 342
DO - 10.5220/0008347403360342
PB - SciTePress