Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach
Existing of autocorrelation will bring a significant effect on the performance and accuracy of process control if the problem does not handle carefully. When dealing with autocorrelated process, Box-Jenkins method will be preferred because of the popularity. However, the computation of Box-Jen...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7018/1/P9919_1fb1df699a1a3afcf9fd57d9eb6b730c.pdf http://eprints.uthm.edu.my/7018/ https://doi.org/10.1088/1742-6596/995/1/012039 |
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| Summary: | Existing of autocorrelation will bring a significant effect on the performance and
accuracy of process control if the problem does not handle carefully. When dealing with
autocorrelated process, Box-Jenkins method will be preferred because of the popularity.
However, the computation of Box-Jenkins method is too complicated and challenging which
cause of time-consuming. Therefore, an alternative method which known as Geometric
Brownian Motion (GBM) is introduced to monitor the autocorrelated process. One real case of
furnace temperature data is conducted to compare the performance of Box-Jenkins and GBM
methods in monitoring autocorrelation process. Both methods give the same results in terms of
model accuracy and monitoring process control. Yet, GBM is superior compared to Box-Jenkins
method due to its simplicity and practically with shorter computational time. |
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