Condition Monitoring with Time Series Data Based on Probabilistic Model

Jaehyun Soh; Dae Eun Kim

doi:10.23919/ICEMS52562.2021.9634481

Condition Monitoring with Time Series Data Based on Probabilistic Model

Jaehyun Soh, Dae Eun Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.

Original language	English
Title of host publication	ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2630-2634
Number of pages	5
ISBN (Electronic)	9788986510218
DOIs	https://doi.org/10.23919/ICEMS52562.2021.9634481
Publication status	Published - 2021
Event	24th International Conference on Electrical Machines and Systems, ICEMS 2021 - Gyeongju, Korea, Republic of Duration: 2021 Oct 31 → 2021 Nov 3

Publication series

Name	ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems

Conference

Conference	24th International Conference on Electrical Machines and Systems, ICEMS 2021
Country/Territory	Korea, Republic of
City	Gyeongju
Period	21/10/31 → 21/11/3

Bibliographical note

Publisher Copyright:
© 2021 KIEE & EMECS.

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Mechanical Engineering
Safety, Risk, Reliability and Quality

Access to Document

10.23919/ICEMS52562.2021.9634481

Cite this

Soh, J., & Kim, D. E. (2021). Condition Monitoring with Time Series Data Based on Probabilistic Model. In ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems (pp. 2630-2634). (ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICEMS52562.2021.9634481

@inproceedings{f39744c2bd5c41f4bc6e4ccd32707c6c,

title = "Condition Monitoring with Time Series Data Based on Probabilistic Model",

abstract = "As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.",

keywords = "Condition monitoring, Condition-based maintenance (CBM), Data selection, Gaussian mixture model (GMM), Prognostics and health management (PHM)",

author = "Jaehyun Soh and Kim, {Dae Eun}",

note = "Publisher Copyright: {\textcopyright} 2021 KIEE & EMECS.; 24th International Conference on Electrical Machines and Systems, ICEMS 2021 ; Conference date: 31-10-2021 Through 03-11-2021",

year = "2021",

doi = "10.23919/ICEMS52562.2021.9634481",

language = "English",

series = "ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2630--2634",

booktitle = "ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems",

address = "United States",

}

Soh, J & Kim, DE 2021, Condition Monitoring with Time Series Data Based on Probabilistic Model. in ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems. ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 2630-2634, 24th International Conference on Electrical Machines and Systems, ICEMS 2021, Gyeongju, Korea, Republic of, 21/10/31. https://doi.org/10.23919/ICEMS52562.2021.9634481

Condition Monitoring with Time Series Data Based on Probabilistic Model. / Soh, Jaehyun; Kim, Dae Eun.
ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2630-2634 (ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Condition Monitoring with Time Series Data Based on Probabilistic Model

AU - Soh, Jaehyun

AU - Kim, Dae Eun

PY - 2021

Y1 - 2021

N2 - As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.

AB - As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.

KW - Condition monitoring

KW - Condition-based maintenance (CBM)

KW - Data selection

KW - Gaussian mixture model (GMM)

KW - Prognostics and health management (PHM)

UR - http://www.scopus.com/inward/record.url?scp=85123932651&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85123932651&partnerID=8YFLogxK

U2 - 10.23919/ICEMS52562.2021.9634481

DO - 10.23919/ICEMS52562.2021.9634481

M3 - Conference contribution

AN - SCOPUS:85123932651

T3 - ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems

SP - 2630

EP - 2634

BT - ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th International Conference on Electrical Machines and Systems, ICEMS 2021

Y2 - 31 October 2021 through 3 November 2021

ER -

Soh J, Kim DE. Condition Monitoring with Time Series Data Based on Probabilistic Model. In ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2630-2634. (ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems). doi: 10.23919/ICEMS52562.2021.9634481

Condition Monitoring with Time Series Data Based on Probabilistic Model

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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Cite this