A framework for multivariate process monitoring and diagnosis
Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing statistical process control frameworks are only effective in shift detection but suffers high false alarm, that is, imbalanc...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
Trans Tech Publications Inc
2013
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.4028/www.scientific.net/AMM.315.374 http://dx.doi.org/10.4028/www.scientific.net/AMM.315.374 http://eprints.uthm.edu.my/3975/1/ibrahim_masood_2.pdf |
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| Summary: | Monitoring and diagnosis of mean shifts in manufacturing processes become more
challenging when involving two or more correlated variables. Unfortunately, most of the existing
statistical process control frameworks are only effective in shift detection but suffers high false
alarm, that is, imbalanced performance monitoring. The problem becomes more complicated when
dealing with small shift particularly in identifying the causable variables. In this research, a
kamework to address balanced monitoring and accurate diagnosis was investigated. Design
considerations involved extensive simulation experiments to select input representation based on
raw data and statistical features, recognizer design structure based on synergistic model, and
monitoring-diagnosis approach based on two stages technique. The study focuses on correlated
process mean shifts for cross correlation function, p = 0.1 - 0.9 and mean shift, p = + 0.75 - 3.00
standard deviations. The proposed design, that is, an Integrated Multivariate Exponentially
Weighted Moving Average with Artificial Neural Network gave superior performance, namely,
average run length, ARLl = 3.18 - 16.75 (for out-of-control process), A& = 452.13 (for incontrol
process) and recognition accuracy, RA = 89.5 - 98.5%. This research has provided a new
perspective in realizing balanced monitoring and accurate diagnosis of multivariate correlated
process mean shifts. |
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