Abstract
As discussed in the previous chapter, control rules are used to detect out-of-control situations by considering the very recent history of a process. However, to avoid such situations, it is necessary to monitor the long-term history as recorded in control charts. Patterns of variations in a control chart can reveal impending out-of-control situations and help to form cause-effect relationships to predict possible abnormalities in a manufacturing process. In this chapter, automatic control chart pattern recognisers utilising heuristic rules and neural networks as well as combinations of these techniques are described. Experimental results show that these systems are capable of identifying patterns and providing early detections of abnormal conditions with a high degree of accuracy.
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