Determining the Contributors for a Multivariate Spc Chart Signal Using Artificial Neural Networks and Support Vector Machine
نویسندگان
چکیده
Due to the rapid change of technology along with advanced data-collection systems, the simultaneous monitoring of two or more quality characteristics (or variables) is necessary. Multivariate Statistical Process Control (SPC) charts are able to effectively detect process disturbances. However, when a disturbance in a multivariate process is triggered by a multivariate SPC chart, process personnel are usually only aware that there are assignable causes causing the multivariate process to be out-of-control. It is very difficult to determine which of the monitored quality characteristics is responsible for this out-of-control signal. This determination is crucial for process improvement, for it can greatly help identify the root causes of the malfunction. As a consequence, this determination becomes a promising research issue in multivariate SPC applications. In this study, we are motivated to propose two mechanisms to solve this difficulty: (1) the integration of the neural network (NN), the Hotelling T 2 SPC chart and RAM; and (2) the integration of the support vector machine (SVM), the Hotelling T 2 SPC chart and RAM. The performance of various process designs was investigated in this study and is compared with the existing RAM method. Using a series of simulations, the results clearly demonstrate greatly enhanced identification rates.
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