A control chart based on cluster-regression adjustment for retrospective monitoring of individual characteristics.
The tendency for experimental and industrial variables to include a certain proportion of outliers has become a rule rather than an exception. These clusters of outliers, if left undetected, have the capability to distort the mean and the covariance matrix of the Hotelling's T2 multivariate control charts constructed to monitor individual quality characteristics. The effect of this distortion is that the control chart constructed from it becomes unreliable as it exhibits masking and swamping, a phenomenon in which an out-of-control process is erroneously declared as an in-control process or an in-control process is erroneously declared as out-of-control process. To handle these problems, this article proposes a control chart that is based on cluster-regression adjustment for retrospective monitoring of individual quality characteristics in a multivariate setting. The performance of the proposed method is investigated through Monte Carlo simulation experiments and historical datasets. Results obtained indicate that the proposed method is an improvement over the state-of-art methods in terms of outlier detection as well as keeping masking and swamping rate under control.
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia.
Hong Choon
HC
Ong
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia.
Ekele
E
Alih
D016208:Databases, Factual; D008962:Models, Theoretical