Deep autoencoder feature extraction for fault detection of elevator systems

Krishna Mishra; Tomi Krogerus; Kalevi Huhtala

Deep autoencoder feature extraction for fault detection of elevator systems

Krishna Mishra
, Tomi Krogerus
, Kalevi Huhtala

Automaatio- ja konetekniikka

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

3 Sitaatiot (Scopus)

Abstrakti

In this research, we propose a generic deep autoencoder model for automated feature extraction from the elevator sensor data. Extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy or faulty based on the maintenance actions recorded. In our research, we have included all fault types present for each elevator. The remaining healthy data is 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 outperform the results using existing features.

Alkuperäiskieli	Englanti
Otsikko	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Alaotsikko	ESANN 2019, Bruges (Belgium), 24 - 26 April 2019
Toimittajat	Michel Verleysen
Julkaisupaikka	Bruges (Belgium)
Kustantaja	i6doc.com publication
Sivut	191-196
Sivumäärä	6
Vuosikerta	27
Painos	2019
ISBN (elektroninen)	978-287-587-066-7
ISBN (painettu)	978-287-587-065-0
Tila	Julkaistu - 24 huhtik. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING - Kesto: 1 tammik. 1900 → …

Conference

Conference	EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING
Ajanjakso	1/01/00 → …

Julkaisufoorumi-taso

Jufo-taso 1

!!ASJC Scopus subject areas

Yleinen tietojenkäsittelytiede

Pääsy asiakirjaan

https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2019

Fault detection of elevator systems using deep autoencoder feature extraction
Mishra, K., Krogerus, T. & Huhtala, K., 31 toukok. 2019, IEEE 13th International Conference on Research Challenges in Information Science: RCIS 2019, 29-31 May 2019, Brussels, Belgium. Heng, S. (toim.). 2019 toim. Brussels, Belgium: IEEE, Vuosikerta 13. s. 43-48 6 Sivumäärä
Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu
13 Sitaatiot (Scopus)
Fault detection of elevator systems using multilayer perceptron neural network
Mishra, K. & Huhtala, K., 13 syysk. 2019, 24th IEEE Conference on Emerging Technologies and Factory Automation: ETFA 2019, September 10-13, 2019 in Zaragoza, Spain. Nogueiras, A. (toim.). 2019 toim. Zaragoza, Spain: IEEE, Vuosikerta 24. s. 904-909 6 Sivumäärä (IEEE Conference on Emerging Technologies and Factory Automation).
Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Open access
30 Sitaatiot (Scopus)
Fault detection of elevator systems using automated feature extraction and classification
Mishra, K., Krogerus, T. & Huhtala, K., 21 toukok. 2018, Elevator Technology 22, Proceedings of Elevcon 2018, 22nd International Congress on Vertical Transportation Technologies: 22-24 May 2018, Berlin, Germany.. 2018 toim. Berlin: The International Association of Elevator Engineers, Vuosikerta 22. s. 116-122 7 Sivumäärä
Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

Siteeraa tätä

Mishra, K., Krogerus, T., & Huhtala, K. (2019). Deep autoencoder feature extraction for fault detection of elevator systems. teoksessa M. Verleysen (Toimittaja), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2019, Bruges (Belgium), 24 - 26 April 2019 (2019 toim., Vuosikerta 27, Sivut 191-196). i6doc.com publication. https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2019

Mishra, Krishna ; Krogerus, Tomi ; Huhtala, Kalevi. / Deep autoencoder feature extraction for fault detection of elevator systems. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2019, Bruges (Belgium), 24 - 26 April 2019. Toimittaja / Michel Verleysen. Vuosikerta 27 2019. toim. Bruges (Belgium) : i6doc.com publication, 2019. Sivut 191-196

@inproceedings{5310e262fe39423f887a8c66b4e2a84f,

title = "Deep autoencoder feature extraction for fault detection of elevator systems",

abstract = "In this research, we propose a generic deep autoencoder model for automated feature extraction from the elevator sensor data. Extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy or faulty based on the maintenance actions recorded. In our research, we have included all fault types present for each elevator. The remaining healthy data is 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 outperform the results using existing features.",

author = "Krishna Mishra and Tomi Krogerus and Kalevi Huhtala",

year = "2019",

month = apr,

day = "24",

language = "English",

isbn = "978-287-587-065-0",

volume = "27",

pages = "191--196",

editor = "Michel Verleysen",

booktitle = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "i6doc.com publication",

edition = "2019",

note = "EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING ; Conference date: 01-01-1900",

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Mishra, K, Krogerus, T & Huhtala, K 2019, Deep autoencoder feature extraction for fault detection of elevator systems. julkaisussa M Verleysen (Toimittaja), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2019, Bruges (Belgium), 24 - 26 April 2019. 2019 toim, Vuosikerta 27, i6doc.com publication, Bruges (Belgium), Sivut 191-196, EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING, 1/01/00. <https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2019>

Deep autoencoder feature extraction for fault detection of elevator systems. / Mishra, Krishna; Krogerus, Tomi; Huhtala, Kalevi.
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2019, Bruges (Belgium), 24 - 26 April 2019. toim. / Michel Verleysen. Vuosikerta 27 2019. toim. Bruges (Belgium): i6doc.com publication, 2019. s. 191-196.

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Deep autoencoder feature extraction for fault detection of elevator systems

AU - Mishra, Krishna

AU - Krogerus, Tomi

AU - Huhtala, Kalevi

PY - 2019/4/24

Y1 - 2019/4/24

N2 - In this research, we propose a generic deep autoencoder model for automated feature extraction from the elevator sensor data. Extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy or faulty based on the maintenance actions recorded. In our research, we have included all fault types present for each elevator. The remaining healthy data is 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 outperform the results using existing features.

AB - In this research, we propose a generic deep autoencoder model for automated feature extraction from the elevator sensor data. Extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy or faulty based on the maintenance actions recorded. In our research, we have included all fault types present for each elevator. The remaining healthy data is 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 outperform the results using existing features.

M3 - Conference contribution

SN - 978-287-587-065-0

VL - 27

SP - 191

EP - 196

BT - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

A2 - Verleysen, Michel

PB - i6doc.com publication

CY - Bruges (Belgium)

T2 - EUROPEAN SYMPOSIUM ON ARTIFICIAL NEURAL NETWORKS, COMPUTATIONAL INTELLIGENCE AND MACHINE LEARNING

Y2 - 1 January 1900

ER -

Mishra K, Krogerus T, Huhtala K. Deep autoencoder feature extraction for fault detection of elevator systems. julkaisussa Verleysen M, toimittaja, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2019, Bruges (Belgium), 24 - 26 April 2019. 2019 toim. Vuosikerta 27. Bruges (Belgium): i6doc.com publication. 2019. s. 191-196

Deep autoencoder feature extraction for fault detection of elevator systems

Abstrakti

Conference

Julkaisufoorumi-taso

!!ASJC Scopus subject areas

Pääsy asiakirjaan

Sormenjälki

Tutkimustuotos

Fault detection of elevator systems using deep autoencoder feature extraction

Fault detection of elevator systems using multilayer perceptron neural network

Fault detection of elevator systems using automated feature extraction and classification

Siteeraa tätä