Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of mul...

Control charts for monitoring of process variance are developed based on Shewhart, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for mean. In all these variance control charts, log transformation of the sample variance is used. The design procedure of this chart is complex and it is poorly understood by the industry. In this paper a EWMA chart for monito...

Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in ...

Most of the data, which is in field network intrusion detection, have characteristics a mixture high-dimensional datasets continuous and categorical variables. It easily leads traditional multivariate control chart to get error detection results. Hotelling's T2 charts based on Principal Component Analysis mix (PCA mix) with bootstrap limit were proposed, applied system. was compared conventiona...

The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the (MCV). In addition, autocorrelation likely to occur processes with high sampling frequency. Hence, designing suitable and investigating effect on these necessary. However, no chart has developed that capable...

Abstract Even if large historical dataset could be available for monitoring key quality features of a process via multivariate control charts, previous knowledge may not enough to reliably identify or adopt unique model all the variables. When no specific parametric turns out appropriate, some alternative solutions should adopted and exploiting non‐parametric methods build chart appears reasona...

In this paper a new technique for monitoring shifts in covariance matrices of Gaussian processes is developed. The processes we monitor are obtained from the covariance matrices estimated using a single observation. These processes follow independent Gaussian distribution in the in-control state, thus allowing for application of standard control charts. Furthermore, in contrary to the existing ...