Multivariate control charts provide control limits for the monitoring of processes and detection of abnormal events so that processes can be improved. However, these multivariate control charts provide limited informa...
详细信息
Multivariate control charts provide control limits for the monitoring of processes and detection of abnormal events so that processes can be improved. However, these multivariate control charts provide limited information about the contribution of any specific variable to the out-of-control alarm. Although many fault isolation methods have been developed to address this deficiency, most of these methods require a parametric distributional assumption that restricts their applicability to specific problems of process control and thus limits their broader usefulness. This study proposes a nonparametric fault isolation method based on a one-class classification algorithm that overcomes the limitation posed by the parametric assumption in existing fault isolation methods. The proposed approach decomposes the monitoring statistics obtained from a one-class classification algorithm into components that reflect the contribution of each variable to the out-of-control signal. A bootstrap approach is used to determine the significance of each variable. A simulation study is presented that examines the performance of the proposed method under various scenarios and to results are compared with those obtained using the T2 decomposition method. The simulation results reveal that the proposed method outperforms the T2 decomposition method in non-normal distribution cases.
In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher pro...
详细信息
In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals;and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-classclassification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-classclassification-based control charts and the traditional Hotelling's T2 chart.
暂无评论