Multivariate Process Monitoring and Diagnosis: A Case Study

Ibrahim Masood; Adnan Hassan

doi:10.4028/www.scientific.net/AMM.315.606

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 315Multivariate Process Monitoring and Diagnosis: A...

Multivariate Process Monitoring and Diagnosis: A Case Study

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.

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Periodical:

Applied Mechanics and Materials (Volume 315)

Pages:

606-611

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.315.606

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Online since:

April 2013

Authors:

Ibrahim Masood, Adnan Hassan

Keywords:

Audio Video Device, Diagnosis, Monitoring, Multivariate Process, Pattern Recognition

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References

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