Extracting relevant features for diagnosing machine tool faults in cloud architecture

Yu Yung Li; Haw Ching Yang; Hao Tieng; Fan Tien Cheng

doi:10.1109/CoASE.2015.7294299

Extracting relevant features for diagnosing machine tool faults in cloud architecture

Yu Yung Li, Haw Ching Yang, Hao Tieng, Fan Tien Cheng

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

Abstract

This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.

Original language	English
Title of host publication	2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publication	Automation for a Sustainable Future, CASE 2015
Publisher	IEEE Computer Society
Pages	1434-1439
Number of pages	6
ISBN (Electronic)	9781467381833
DOIs	https://doi.org/10.1109/CoASE.2015.7294299
Publication status	Published - 2015 Oct 7
Event	11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden Duration: 2015 Aug 24 → 2015 Aug 28

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2015-October
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Other

Other	11th IEEE International Conference on Automation Science and Engineering, CASE 2015
Country/Territory	Sweden
City	Gothenburg
Period	15-08-24 → 15-08-28

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/CoASE.2015.7294299

Cite this

Li, Y. Y., Yang, H. C., Tieng, H., & Cheng, F. T. (2015). Extracting relevant features for diagnosing machine tool faults in cloud architecture. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015 (pp. 1434-1439). Article 7294299 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294299

@inproceedings{d6150771707a433982b9cd80c65edaa8,

title = "Extracting relevant features for diagnosing machine tool faults in cloud architecture",

abstract = "This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.",

author = "Li, {Yu Yung} and Yang, {Haw Ching} and Hao Tieng and Cheng, {Fan Tien}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th IEEE International Conference on Automation Science and Engineering, CASE 2015 ; Conference date: 24-08-2015 Through 28-08-2015",

year = "2015",

month = oct,

day = "7",

doi = "10.1109/CoASE.2015.7294299",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

publisher = "IEEE Computer Society",

pages = "1434--1439",

booktitle = "2015 IEEE Conference on Automation Science and Engineering",

address = "United States",

}

Li, YY, Yang, HC, Tieng, H & Cheng, FT 2015, Extracting relevant features for diagnosing machine tool faults in cloud architecture. in 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015., 7294299, IEEE International Conference on Automation Science and Engineering, vol. 2015-October, IEEE Computer Society, pp. 1434-1439, 11th IEEE International Conference on Automation Science and Engineering, CASE 2015, Gothenburg, Sweden, 15-08-24. https://doi.org/10.1109/CoASE.2015.7294299

Extracting relevant features for diagnosing machine tool faults in cloud architecture. / Li, Yu Yung; Yang, Haw Ching; Tieng, Hao et al.
2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society, 2015. p. 1434-1439 7294299 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October).

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

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AU - Cheng, Fan Tien

PY - 2015/10/7

Y1 - 2015/10/7

N2 - This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.

AB - This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.

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M3 - Conference contribution

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T3 - IEEE International Conference on Automation Science and Engineering

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BT - 2015 IEEE Conference on Automation Science and Engineering

PB - IEEE Computer Society

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Li YY, Yang HC, Tieng H , Cheng FT. Extracting relevant features for diagnosing machine tool faults in cloud architecture. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society. 2015. p. 1434-1439. 7294299. (IEEE International Conference on Automation Science and Engineering). doi: 10.1109/CoASE.2015.7294299

Extracting relevant features for diagnosing machine tool faults in cloud architecture

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