Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Independent component analysis: algorithms and applications
Neural Networks
Statistical Control by Monitoring and Feedback Adjustment
Statistical Control by Monitoring and Feedback Adjustment
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
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In the past few years, many studies have been conducted about the integrated use of statistical process control (SPC) and engineering process control (EPC) because applying them separately cannot control the manufacturing process optimally. And most of these studies reported that the integration outperforms than using only SPC or EPC. Basically, the former aims to rapidly detect assignable causes and time points for abnormalities that take place during process; and the latter is a method in which input variables are adjusted against process outputs through a feedback control mechanism. However, though combining SPC with EPC can effectively detect time points when abnormalities occur during process, using these two conventional tools may give false alarms in most cases if process data are auto-correlated to some extent. In this study, to increase the accuracy of process disturbance identification, we used independent component analysis (ICA) with classification and regression tree (CART) approach to identify and recognize shifts in the correlated process parameters.