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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| Format: | Thesis |
| Language: | en |
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2012
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| Online Access: | http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf http://eprints.uthm.edu.my/2539/ |
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| author | Masood, Ibrahim |
| author_facet | Masood, Ibrahim |
| author_sort | Masood, Ibrahim |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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 |
| format | Thesis |
| id | my.uthm.eprints-2539 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2012 |
| record_format | eprints |
| spelling | my.uthm.eprints-25392021-11-01T02:34:53Z http://eprints.uthm.edu.my/2539/ A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts Masood, Ibrahim TS Manufactures TS155-194 Production management. Operations management 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 2012-01 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf Masood, Ibrahim (2012) A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts. Doctoral thesis, Universiti Teknologi Malaysia. |
| spellingShingle | TS Manufactures TS155-194 Production management. Operations management Masood, Ibrahim A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title | A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title_full | A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title_fullStr | A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title_full_unstemmed | A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title_short | A scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| title_sort | scheme for balanced monitoring and accurate diagnosis of bivariate process mean shifts |
| topic | TS Manufactures TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/2539/1/24p%20IBRAHIM%20MASOOD.pdf http://eprints.uthm.edu.my/2539/ |
| url_provider | http://eprints.uthm.edu.my/ |