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Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches

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  • Yuehjen E. Shao

Abstract

Because of the excellent performance on monitoring and controlling an autocorrelated process, the integration of statistical process control (SPC) and engineering process control (EPC) has drawn considerable attention in recent years. Both theoretical and empirical findings have suggested that the integration of SPC and EPC can be an effective way to improve the quality of a process, especially when the underlying process is autocorrelated. However, because EPC compensates for the effects of underlying disturbances, the disturbance patterns are embedded and hard to be recognized. Effective recognition of disturbance patterns is a very important issue for process improvement since disturbance patterns would be associated with certain assignable causes which affect the process. In practical situations, after compensating by EPC, the underlying disturbance patterns could be of any mixture types which are totally different from the original patterns. This study proposes the integration of support vector machine (SVM) and artificial neural network (ANN) approaches to recognize the disturbance patterns of the underlying disturbances. Experimental results revealed that the proposed schemes are able to effectively recognize various disturbance patterns of an SPC/EPC system.

Suggested Citation

  • Yuehjen E. Shao, 2014. "Recognition of Process Disturbances for an SPC/EPC Stochastic System Using Support Vector Machine and Artificial Neural Network Approaches," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:519705
    DOI: 10.1155/2014/519705
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    References listed on IDEAS

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    1. Yuehjen E. Shao & Chia-Ding Hou, 2013. "Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
    2. Aslan Deniz Karaoglan & Gunhan Mirac Bayhan, 2014. "A regression control chart for autocorrelated processes," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 16(2), pages 238-256.
    3. Yuehjen E. Shao & Chia-Ding Hou, 2013. "Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-10, December.
    4. José Gomes Requeijo & Joana Cordeiro, 2013. "Implementation of the statistical process control with autocorrelated data in an automotive manufacturer," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 13(3), pages 325-344.
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