Automatic baseline-sample-selection scheme for baseline predictive maintenance

Chun Fang Chen, Yao Sheng Hsieh, Fan Tien Cheng, Hsien Cheng Huang, Saint Chi Wang

研究成果: Conference contribution

1 引文 (Scopus)

摘要

A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating the target-device (TD) baseline model consist of the concise and healthy (C&H) historical samples and the fresh samples just after maintenance. Originally, each one of the C&H samples was checked manually to ensure that the sample was generated under healthy status and its data quality is good. However, this health-&-quality check process is so tedious and may also neglect deleting contradictory samples, which may deteriorate the BPM results and prohibit the usage of the BPM scheme. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme for selecting the C&H samples and deleting the contradictory samples automatically.

原文English
主出版物標題2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
頁面183-188
頁數6
DOIs
出版狀態Published - 2013 十二月 1
事件2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 - Madison, WI, United States
持續時間: 2013 八月 172013 八月 20

出版系列

名字IEEE International Conference on Automation Science and Engineering
ISSN(列印)2161-8070
ISSN(電子)2161-8089

Other

Other2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
國家United States
城市Madison, WI
期間13-08-1713-08-20

指紋

Failure analysis
Health

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

引用此文

Chen, C. F., Hsieh, Y. S., Cheng, F. T., Huang, H. C., & Wang, S. C. (2013). Automatic baseline-sample-selection scheme for baseline predictive maintenance. 於 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 (頁 183-188). [6653934] (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/CoASE.2013.6653934
Chen, Chun Fang ; Hsieh, Yao Sheng ; Cheng, Fan Tien ; Huang, Hsien Cheng ; Wang, Saint Chi. / Automatic baseline-sample-selection scheme for baseline predictive maintenance. 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013. 2013. 頁 183-188 (IEEE International Conference on Automation Science and Engineering).
@inproceedings{077134fe236c4fb786eb56558725aa77,
title = "Automatic baseline-sample-selection scheme for baseline predictive maintenance",
abstract = "A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating the target-device (TD) baseline model consist of the concise and healthy (C&H) historical samples and the fresh samples just after maintenance. Originally, each one of the C&H samples was checked manually to ensure that the sample was generated under healthy status and its data quality is good. However, this health-&-quality check process is so tedious and may also neglect deleting contradictory samples, which may deteriorate the BPM results and prohibit the usage of the BPM scheme. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme for selecting the C&H samples and deleting the contradictory samples automatically.",
author = "Chen, {Chun Fang} and Hsieh, {Yao Sheng} and Cheng, {Fan Tien} and Huang, {Hsien Cheng} and Wang, {Saint Chi}",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/CoASE.2013.6653934",
language = "English",
isbn = "9781479915156",
series = "IEEE International Conference on Automation Science and Engineering",
pages = "183--188",
booktitle = "2013 IEEE International Conference on Automation Science and Engineering, CASE 2013",

}

Chen, CF, Hsieh, YS, Cheng, FT, Huang, HC & Wang, SC 2013, Automatic baseline-sample-selection scheme for baseline predictive maintenance. 於 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013., 6653934, IEEE International Conference on Automation Science and Engineering, 頁 183-188, 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013, Madison, WI, United States, 13-08-17. https://doi.org/10.1109/CoASE.2013.6653934

Automatic baseline-sample-selection scheme for baseline predictive maintenance. / Chen, Chun Fang; Hsieh, Yao Sheng; Cheng, Fan Tien; Huang, Hsien Cheng; Wang, Saint Chi.

2013 IEEE International Conference on Automation Science and Engineering, CASE 2013. 2013. p. 183-188 6653934 (IEEE International Conference on Automation Science and Engineering).

研究成果: Conference contribution

TY - GEN

T1 - Automatic baseline-sample-selection scheme for baseline predictive maintenance

AU - Chen, Chun Fang

AU - Hsieh, Yao Sheng

AU - Cheng, Fan Tien

AU - Huang, Hsien Cheng

AU - Wang, Saint Chi

PY - 2013/12/1

Y1 - 2013/12/1

N2 - A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating the target-device (TD) baseline model consist of the concise and healthy (C&H) historical samples and the fresh samples just after maintenance. Originally, each one of the C&H samples was checked manually to ensure that the sample was generated under healthy status and its data quality is good. However, this health-&-quality check process is so tedious and may also neglect deleting contradictory samples, which may deteriorate the BPM results and prohibit the usage of the BPM scheme. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme for selecting the C&H samples and deleting the contradictory samples automatically.

AB - A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating the target-device (TD) baseline model consist of the concise and healthy (C&H) historical samples and the fresh samples just after maintenance. Originally, each one of the C&H samples was checked manually to ensure that the sample was generated under healthy status and its data quality is good. However, this health-&-quality check process is so tedious and may also neglect deleting contradictory samples, which may deteriorate the BPM results and prohibit the usage of the BPM scheme. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme for selecting the C&H samples and deleting the contradictory samples automatically.

UR - http://www.scopus.com/inward/record.url?scp=84891534648&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891534648&partnerID=8YFLogxK

U2 - 10.1109/CoASE.2013.6653934

DO - 10.1109/CoASE.2013.6653934

M3 - Conference contribution

AN - SCOPUS:84891534648

SN - 9781479915156

T3 - IEEE International Conference on Automation Science and Engineering

SP - 183

EP - 188

BT - 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

ER -

Chen CF, Hsieh YS, Cheng FT, Huang HC, Wang SC. Automatic baseline-sample-selection scheme for baseline predictive maintenance. 於 2013 IEEE International Conference on Automation Science and Engineering, CASE 2013. 2013. p. 183-188. 6653934. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/CoASE.2013.6653934