Abstract The ability to test for statistical causality in linear and nonlinear contexts, stationary or non-stationary settings, identify whether influences trend of volatility forms a particularly important class problems explore multi-modal multivariate processes. In this paper, we develop novel testing frameworks general classes time series models. Our framework accommodates flexible features...

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been ...

In this paper a multivariate quality control procedure is offered in which several correlated stages are present in production systems and in each stage there are several correlated control variable. Using Hotelling's T2 statistic, first, each stage is tested for being out of control. Then out of control variables are selected using Murphy's method. The remainder of the research involves evalua...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, f...

This paper deals with the application of Principal Component Analysis (PCA) and the Hotelling’s T2 Chart, using data collected from a drinking water treatment process. PCA is applied primarily for the dimensional reduction of the collected data. The Hotelling’s T2 control chart was used for the fault detection of the process. The data was taken from a United Utilities Multistage Water Treatment...

when a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. however, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...