Abstract
To consolidate the balance of sustained generator and diminished maintenance cost in the power plant is one of the most desired goals of a plant administrator. This goal, in fact, can be fulfilled by a proper turbine maintenance policy. This study presented the use of total productive maintenance and the development of turbine prevention maintenance models can enhance the efficiency of equipments. The probabilistic failure analysis model can determine the maintenance cycle and best maintenance time of turbine by data analysis. In addition, it is shown that applying the radio frequency identification (RFID) in a prevention maintenance operational model could generate cost-saving effectiveness.
| Original language | English |
|---|---|
| Title of host publication | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
| Pages | 501-505 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2008 Dec 1 |
| Event | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 - Singapore, Singapore Duration: 2008 Dec 8 → 2008 Dec 11 |
Publication series
| Name | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
|---|
Other
| Other | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 08-12-08 → 08-12-11 |
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All Science Journal Classification (ASJC) codes
- Management Information Systems
- Industrial and Manufacturing Engineering
Cite this
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Optimize a RFID-based turbine maintenance model - A preliminary study. / Lyu, Jrjung; Chen, Tung Liang.
2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008. 2008. p. 501-505 4737919 (2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Optimize a RFID-based turbine maintenance model - A preliminary study
AU - Lyu, Jrjung
AU - Chen, Tung Liang
PY - 2008/12/1
Y1 - 2008/12/1
N2 - To consolidate the balance of sustained generator and diminished maintenance cost in the power plant is one of the most desired goals of a plant administrator. This goal, in fact, can be fulfilled by a proper turbine maintenance policy. This study presented the use of total productive maintenance and the development of turbine prevention maintenance models can enhance the efficiency of equipments. The probabilistic failure analysis model can determine the maintenance cycle and best maintenance time of turbine by data analysis. In addition, it is shown that applying the radio frequency identification (RFID) in a prevention maintenance operational model could generate cost-saving effectiveness.
AB - To consolidate the balance of sustained generator and diminished maintenance cost in the power plant is one of the most desired goals of a plant administrator. This goal, in fact, can be fulfilled by a proper turbine maintenance policy. This study presented the use of total productive maintenance and the development of turbine prevention maintenance models can enhance the efficiency of equipments. The probabilistic failure analysis model can determine the maintenance cycle and best maintenance time of turbine by data analysis. In addition, it is shown that applying the radio frequency identification (RFID) in a prevention maintenance operational model could generate cost-saving effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=62749140086&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62749140086&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2008.4737919
DO - 10.1109/IEEM.2008.4737919
M3 - Conference contribution
AN - SCOPUS:62749140086
SN - 9781424426300
T3 - 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
SP - 501
EP - 505
BT - 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
ER -