부산대학교 도서관

Among the anomaly detection methods, control charts have been considered important techniques. In practice, however, even under the normal behaviour of the data, the standard deviation of the sequence is not stable. In such cases, the coefficient of variation (CV) is a more appropriate measure for assessing system stability. In this paper, we consider the statistical design of Run Rules-based control charts for monitoring the CV of multivariate data. A Markov chain approach is used to evaluate the statistical performance of the proposed charts. The computational results show that the Run Rules-based charts outperform the standard Shewhart control chart significantly. Moreover, by choosing an appropriate scheme, the Run Rules-based charts perform better than the Run Sum control chart for monitoring the multivariate CV.