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Multivariate process monitoring and diagnosis: a case study

Masood, Ibrahim and Hassan, Adnan (2013) Multivariate process monitoring and diagnosis: a case study. Applied Mechanics and Materials , 315. pp. 606-611. ISSN 1660-9336

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: audio video device; multivariate process; monitoring and diagnosis; pattern recognition
Subjects: T Technology > TS Manufactures > TS155-194 Production management. Operations management
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Manufacturing and Industrial Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 02 Jul 2013 07:02
Last Modified: 02 Jul 2013 07:02
URI: http://eprints.uthm.edu.my/id/eprint/3974
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