完整後設資料紀錄
DC 欄位語言
dc.contributor.authorLin, Chun-Chengen_US
dc.contributor.authorDeng, Der-Jiunnen_US
dc.contributor.authorKang, Jia-Rongen_US
dc.contributor.authorChang, Sen-Chiaen_US
dc.contributor.authorChueh, Chuang-Huaen_US
dc.date.accessioned2017-04-21T06:56:12Z-
dc.date.available2017-04-21T06:56:12Z-
dc.date.issued2016-12en_US
dc.identifier.issn1551-3203en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TII.2015.2513764en_US
dc.identifier.urihttp://hdl.handle.net/11536/133264-
dc.description.abstractIn the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.en_US
dc.language.isoen_USen_US
dc.subjectCondition-based maintenance (CBM)en_US
dc.subjectdata miningen_US
dc.subjectgrey forecastingen_US
dc.subjectindustrial sensoren_US
dc.subjectlight emitting diode (LED) manufacturingen_US
dc.subjectrare event detectionen_US
dc.titleForecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approachen_US
dc.identifier.doi10.1109/TII.2015.2513764en_US
dc.identifier.journalIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICSen_US
dc.citation.volume12en_US
dc.citation.issue6en_US
dc.citation.spage2228en_US
dc.citation.epage2235en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000391299700025en_US
顯示於類別:期刊論文