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 | |
| 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 | Sweden |
| City | Gothenburg |
| Period | 15-08-24 → 15-08-28 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
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Extracting relevant features for diagnosing machine tool faults in cloud architecture. / Li, Yu Yung; Yang, Haw Ching; Tieng, Hao; Cheng, Fan-Tien.
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
TY - GEN
T1 - Extracting relevant features for diagnosing machine tool faults in cloud architecture
AU - Li, Yu Yung
AU - Yang, Haw Ching
AU - Tieng, Hao
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.
UR - http://www.scopus.com/inward/record.url?scp=84952777871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84952777871&partnerID=8YFLogxK
U2 - 10.1109/CoASE.2015.7294299
DO - 10.1109/CoASE.2015.7294299
M3 - Conference contribution
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1434
EP - 1439
BT - 2015 IEEE Conference on Automation Science and Engineering
PB - IEEE Computer Society
ER -