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Control charts is an important tool of statistical quality control (SQC), and the recognition of mixed abnormal pattern exists on the control chart is one of difficulties of on-line intelligent process quality diagnosis. After limitations of control chart-recognizers used in practice were analyzed, a novel intelligent process quality diagnosis method is proposed with a special model of multi-class...
As one of the primary Statistical Process Control (SPC) tools, control chart plays a very important role in attaining process stability. There are many cases in which the simultaneous monitoring or control of two or more related quality characteristics is required. Out-of-control signals in multivariate charts may be caused by one or more variables or a set of variables. One difficulty encountered...
A new approach using class-incremental Fisher discriminant analysis (FDA) with principal component analysis (PCA) is proposed for process monitoring. FDA seeks directions that are efficient for discrimination and shows smaller error rate for detecting and diagnosing the known faults. However, it may not detect and identify those new faults that aren't included in the model. As its complementarities,...
Many of the previous research on the control chart pattern recognition were related to fully developed patterns. However, in practice, the process data will appear as a continuous stream of partially developed patterns. Such developing patterns are difficult to recognize since their structure are normally vague and dynamic. This study investigated the merit of a generalized single recognizer (all-class-one-network,...
In a control chart, unnatural patterns are always associated with some specific assignable causes that should be eliminated. The identification of control chart pattern (CCP) is therefore important and further estimation of the unnatural pattern parameters can improve the manufacturing process. In this paper, a modified counter-propagation network (m-CPN) is developed to classify the mean shift and...
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