Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach

Chun Cheng Lin, Der-Jiunn Deng, Jia Rong Kang, Sen Chia Chang, Chuang Hua Chueh

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2228-2235
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume12
Issue number6
DOIs
Publication statusPublished - 2016 Dec 1

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Epitaxial growth
Light emitting diodes
Aging of materials
Pneumatics
Probability distributions
Numerical analysis
Statistics
Defects
Sensors
Industry

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach. / Lin, Chun Cheng; Deng, Der-Jiunn; Kang, Jia Rong; Chang, Sen Chia; Chueh, Chuang Hua.

In: IEEE Transactions on Industrial Informatics, Vol. 12, No. 6, 01.12.2016, p. 2228-2235.

Research output: Contribution to journalArticle

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