標題: Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach
作者: Lin, Chun-Cheng
Deng, Der-Jiunn
Kang, Jia-Rong
Chang, Sen-Chia
Chueh, Chuang-Hua
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Condition-based maintenance (CBM);data mining;grey forecasting;industrial sensor;light emitting diode (LED) manufacturing;rare event detection
公開日期: Dec-2016
摘要: In 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.
URI: http://dx.doi.org/10.1109/TII.2015.2513764
http://hdl.handle.net/11536/133335
ISSN: 1551-3203
DOI: 10.1109/TII.2015.2513764
期刊: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume: 12
Issue: 6
起始頁: 2228
結束頁: 2235
Appears in Collections:Articles