An Application of Supervised Multivariate Control Charts on a Power Plant Data

摘要

We propose a supervised data mining approach that takes advantage of available out-of-control data. By using classification techniques, our proposed approach can utilize both the in-control and out-of-control data information to construct a control chart. The probability that a data point belongs to a certain class from the classification result is plotted and the control limits can be established. A real power plant data set is used to illustrate our proposed approach. The comparative study shows that our proposed approach performs better than traditional multivariate control chart techniques.