Multivariate process monitoring and diagnosis: a case study
In manufacturing industries, monitoring and diagnosis of multivariate process out-of control condition become more challenging. Process monitoring refers to the identification of process status either it is running within a statistically in-control or out-of-control condition, whereas process diagno...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Published: |
Trans Tech Publications Inc
2013
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4028/www.scientific.net/AMM.315.606 http://dx.doi.org/10.4028/www.scientific.net/AMM.315.606 http://eprints.uthm.edu.my/3974/1/ibrahim_masood.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In manufacturing industries, monitoring and diagnosis of multivariate process out-of control
condition become more challenging. Process monitoring refers to the identification of
process status either it is running within a statistically in-control or out-of-control condition,
whereas process diagnosis refers to the identification of the source variables of out-of-control
process. In order to achieve these requirements, the application of an appropriate statistical process
control framework is necessary for rapidly and accurately identifying the signs and source out-of contol
condition with minimum false alarm. In this research, a framework namely, an Integrated
Multivariate Exponentially Weighted Moving Average with Artificial Neural Network was
investigated in monitoring-diagnosis of multivariate process mean shifts in manufacturing audio
video device component. Based on two-stages monitoring-diagnosis technique, the proposed
framework has resulted in efficient performance. |
|---|