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Fault detection of elevator systems using automated feature extraction and classification

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

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Fault detection of elevator systems using automated feature extraction and classification. / Mishra, Krishna; Krogerus, Tomi; Huhtala, Kalevi.

Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany.. Vol. 22 2018. ed. Berlin : The International Association of Elevator Engineers, 2018. p. 116-122.

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

Harvard

Mishra, K, Krogerus, T & Huhtala, K 2018, Fault detection of elevator systems using automated feature extraction and classification. in Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany.. 2018 edn, vol. 22, The International Association of Elevator Engineers, Berlin, pp. 116-122, International Congress on Vertical Transportation Technologies, 27/06/18.

APA

Mishra, K., Krogerus, T., & Huhtala, K. (2018). Fault detection of elevator systems using automated feature extraction and classification. In Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany. (2018 ed., Vol. 22, pp. 116-122). Berlin: The International Association of Elevator Engineers.

Vancouver

Mishra K, Krogerus T, Huhtala K. Fault detection of elevator systems using automated feature extraction and classification. In Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany.. 2018 ed. Vol. 22. Berlin: The International Association of Elevator Engineers. 2018. p. 116-122

Author

Mishra, Krishna ; Krogerus, Tomi ; Huhtala, Kalevi. / Fault detection of elevator systems using automated feature extraction and classification. Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany.. Vol. 22 2018. ed. Berlin : The International Association of Elevator Engineers, 2018. pp. 116-122

Bibtex - Download

@inproceedings{7ddd90ed8fc04c2c9185bd77616e6081,
title = "Fault detection of elevator systems using automated feature extraction and classification",
abstract = "In this research, we study an automated feature extraction technique to calculate new features from raw sensor data provided by an elevator data recording system and to create a more generic machine learning model for fault detection. Another data set called maintenance data is used to find the time period for creating class variables. The calculated features attached to class variables are classified as healthy or faulty using random forest algorithm. The time period starts from a fault reported by the customer and ends when maintenance is finished and reported. We use accuracy, sensitivity and specificity as evaluation parameters for this research.",
keywords = "Machine learning, elevator system, deep learning, classification, fault detection, feature extraction",
author = "Krishna Mishra and Tomi Krogerus and Kalevi Huhtala",
year = "2018",
month = "5",
day = "21",
language = "English",
isbn = "978-965-572-261-1",
volume = "22",
pages = "116--122",
booktitle = "Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies",
publisher = "The International Association of Elevator Engineers",
edition = "2018",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Fault detection of elevator systems using automated feature extraction and classification

AU - Mishra, Krishna

AU - Krogerus, Tomi

AU - Huhtala, Kalevi

PY - 2018/5/21

Y1 - 2018/5/21

N2 - In this research, we study an automated feature extraction technique to calculate new features from raw sensor data provided by an elevator data recording system and to create a more generic machine learning model for fault detection. Another data set called maintenance data is used to find the time period for creating class variables. The calculated features attached to class variables are classified as healthy or faulty using random forest algorithm. The time period starts from a fault reported by the customer and ends when maintenance is finished and reported. We use accuracy, sensitivity and specificity as evaluation parameters for this research.

AB - In this research, we study an automated feature extraction technique to calculate new features from raw sensor data provided by an elevator data recording system and to create a more generic machine learning model for fault detection. Another data set called maintenance data is used to find the time period for creating class variables. The calculated features attached to class variables are classified as healthy or faulty using random forest algorithm. The time period starts from a fault reported by the customer and ends when maintenance is finished and reported. We use accuracy, sensitivity and specificity as evaluation parameters for this research.

KW - Machine learning, elevator system, deep learning, classification, fault detection, feature extraction

UR - http://www.elevcon.com/

M3 - Conference contribution

SN - 978-965-572-261-1

VL - 22

SP - 116

EP - 122

BT - Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies

PB - The International Association of Elevator Engineers

CY - Berlin

ER -