A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts
Monitoring and diagnosis of mean shifts in manufacturing processes become more challenging when involving two or more correlated variables. Unfortunately, most of the existing multivariate statistical process control schemes are only effective in rapid detection but suffer high false alarm. Th...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf http://eprints.uthm.edu.my/2539/ |
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| Institution: | Universiti Tun Hussein Onn Malaysia |
| Language: | English |
| 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 multivariate statistical process control schemes are only effective
in rapid detection but suffer high false alarm. This is referred to as imbalanced
performance monitoring. The problem becomes more complicated when dealing with
small mean shift particularly in identifying the causable variables. In this research, a
scheme to enable balanced monitoring and accurate diagnosis was investigated in
order to improve such limitations. Design considerations involved extensive
simulation experiments to select input representation based on raw data and statistical
features, recognizer design structure based on individual and synergistic models, and
monitoring-diagnosis approach based on single stage and two stages techniques. The
study focuses on correlated process mean shifts for cross correlation function, ρ = 0.1
~ 0.9 and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated
designs, an Integrated Multivariate Exponentially Weighted Moving Average with
Artificial Neural Network scheme gave superior performance, namely, average run
lengths, ARL1 = 3.18 ~ 16.75 (for out-of-control process) and ARL0 = 452.13 (for in�control process), and recognition accuracy, RA = 89.5 ~ 98.5%. The proposed
scheme was validated using an industrial case study from machining process of
audio-video device component. This research has provided a new perspective in
realizing balanced monitoring and accurate diagnosis of correlated process mean
shifts |
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