Intergration of control chart and pattern recognizer for bivariate quality control
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...
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| Format: | Thesis |
| Language: | English English English |
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2015
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| Online Access: | http://eprints.uthm.edu.my/1413/ http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf |
| _version_ | 1848887453827465216 |
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| author | Mohd Sohaimi, Nurul Adlihisam |
| author_facet | Mohd Sohaimi, Nurul Adlihisam |
| author_sort | Mohd Sohaimi, Nurul Adlihisam |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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 that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN 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, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 7.78 ( for out-of-control) and ARL0 = 491.03 (small mean shift) and 524.80 (large mean shift) in control process and the grand average for recognition accuracy (RA) = 96.36 98.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts. |
| first_indexed | 2025-11-15T19:54:38Z |
| format | Thesis |
| id | uthm-1413 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:54:38Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-14132021-10-03T06:46:26Z http://eprints.uthm.edu.my/1413/ Intergration of control chart and pattern recognizer for bivariate quality control Mohd Sohaimi, Nurul Adlihisam 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 that integrated the control charting and pattern recognition technique has been investigated toward improving the quality control (QC) performance. Design considerations involved extensive simulation experiments to select input representation based on raw data and statistical features, recognizer design structure based on individual and Statistical Features-ANN 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, 0.5, 0.9, and mean shift, μ = ± 0.75 ~ 3.00 standard deviations. Among the investigated design, an Integrated Multivariate Exponentially Weighted Moving Average with Artificial Neural Network scheme provides superior performance, namely the Average Run Length for grand average ARL1 = 7.55 7.78 ( for out-of-control) and ARL0 = 491.03 (small mean shift) and 524.80 (large mean shift) in control process and the grand average for recognition accuracy (RA) = 96.36 98.74. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis of correlated process mean shifts. 2015-06 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf text en http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf Mohd Sohaimi, Nurul Adlihisam (2015) Intergration of control chart and pattern recognizer for bivariate quality control. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | TS155-194 Production management. Operations management Mohd Sohaimi, Nurul Adlihisam Intergration of control chart and pattern recognizer for bivariate quality control |
| title | Intergration of control chart and pattern recognizer for bivariate quality control |
| title_full | Intergration of control chart and pattern recognizer for bivariate quality control |
| title_fullStr | Intergration of control chart and pattern recognizer for bivariate quality control |
| title_full_unstemmed | Intergration of control chart and pattern recognizer for bivariate quality control |
| title_short | Intergration of control chart and pattern recognizer for bivariate quality control |
| title_sort | intergration of control chart and pattern recognizer for bivariate quality control |
| topic | TS155-194 Production management. Operations management |
| url | http://eprints.uthm.edu.my/1413/ http://eprints.uthm.edu.my/1413/2/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1413/1/24p%20NURUL%20ADLIHISAM%20MOHD%20SOHAIMI.pdf http://eprints.uthm.edu.my/1413/3/NURUL%20ADLIHISAM%20MOHD%20SOHAIMI%20WATERMARK.pdf |