Study of artificial neural network scheme application in manufacturing industry for monitoring-diagnosis bivariate process variation
In manufacturing industries, process variation is known to be a major source of poor quality. As such, process monitoring and diagnosis is critical towards continuous quality improvement. This becomes more challenging when involving two or more correlated variables (multivariate). Process moni...
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| フォーマット: | 学位論文 |
| 言語: | English English English |
| 出版事項: |
2014
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/1531/1/24p%20MARIAM%20MAJID.pdf http://eprints.uthm.edu.my/1531/2/MARIAM%20MAJID%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1531/3/MARIAM%20MAJID%20WATERMARK.pdf http://eprints.uthm.edu.my/1531/ |
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| 要約: | In manufacturing industries, process variation is known to be a major source of
poor quality. As such, process monitoring and diagnosis is critical towards continuous
quality improvement. This becomes more challenging when involving two or more
correlated variables (multivariate). Process monitoring refers to the identification of
process status either it is running within a statistically in-control or out-of-control
condition, whereas process diagnosis refers to the identification of the source variables
of out-of-control process. The traditional statistical process control (SPC) charting
schemes are known to be effective in monitoring aspect. Nevertheless, they are lack of
diagnosis. In recent years, the artificial neural network (ANN) based pattern recognition
schemes have been developed for solving this issue. The existing schemes are mainly
designed for dealing with fully completed process data streams. In practice, however,
there are cases that observation data are incomplete due to measurement error. In this
research, an ensemble (combined) ANN model pattern recognizer will be investigated
for recognizing data streams process. Each model consists of different input
representation, namely, raw data and statistical features. The raw data of representation
generate by manufacturing industry as a real data. The proposed ensemble ANN scheme
would provide better perspective in this research area. |
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