online_monitor: Online Monitoring of Serially Correlated Data

In XiulinXie/SPCmonitor2: Process Control Chart for Monitoring Short-Range Serially Correlated Data

Description

online_monitor() estimats the correlation structure nonparametrically from an IC dataset, and decorrelating the original process observations. After data decorrelation, a univariate nonparametric CUSUM chart based on data categorization was applied to monitor the new data set to detect if there is any mean shift occurs, and give us a signal as soon as possible.

Usage

online_monitor(x1, xx, h = 10, k = 0.01, bmax = 10)

Arguments

x1	the in control data set
xx	the new data needed to monitor
h	the control lomit
k	the allowance value
bmax	The smallest number that any data points have no or small correlations with previous data points which means γ(q) ≈ 0 when q > bmax

Details

Traditional statistical process control charts are based on the assumptions that process observations are independent and identically normally distributed when the related process is in-control (IC). online_monitor() applied a general charting scheme for monitoring serially correlated process observations with short-memory data dependence and unknown process distributions. The method focus on Phase II online monitoring of process observations X1,X2,...,Xn, where n ≥ 1 is the current time point during process monitoring. The IC process distribution is assumed to be unknown, and the process observations are serially correlated. The output of the function online_monitor() is the index of the obsevation in the new data that gives a out of control signal. For example, if the output is 14, the result indicates that at X14, the function detects a mean shift.

Value

The index of the obsevation in the new data that gives a mean shift signal

Author(s)

Xiulin Xie

Examples

online_monitor(x1 = rnorm(200,0,1),xx = rnorm(100,10,1) ,h=20,k=0.01)

XiulinXie/SPCmonitor2 documentation built on Dec. 10, 2019, 12:10 a.m.