scholarly journals Change Point Detection with Robust Control Chart

2011 ◽  
Vol 2011 ◽  
pp. 1-20
Author(s):  
Ng Kooi Huat ◽  
Habshah Midi
Keyword(s):  
Robust Control ◽  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Process ◽  
Change Point Detection ◽  
Process Data ◽  
To Come ◽  
Point Detection ◽  
Control Limits

Monitoring a process over time using a control chart allows quick detection of unusual states. In phase I, some historical process data, assumed to come from an in-control process, are used to construct the control limits. In Phase II, the process is monitored for an ongoing basis using control limits from Phase I. In Phase II, observations falling outside the control limits or unusual patterns of observations signal that the process has shifted from in-control process settings. Such signals trigger a search for assignable cause and, if the cause is found, corrective action will be implemented to prevent its recurrence. The purpose of this paper is to introduce a new methodology appropriate for constructing a robust control chart when a nonnormal or a contaminated data that may arise in phase I state. Through extensive Monte Carlo simulations, we examine the behaviors and performances of the proposed MM robust control chart when there is a process shift in mean.

Robust approaches for monitoring logistic regression profiles under outliers

2017 ◽  
Vol 34 (4) ◽  
pp. 494-507 ◽  
Author(s):  
Ahmad Hakimi ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Logistic Regression ◽  
Robust Control ◽  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
Likelihood Estimation ◽  
Estimation Of Parameters ◽  
Content Type ◽  
Practical Applications

Purpose The purpose of this paper is to develop some robust approaches to estimate the logistic regression profile parameters in order to decrease the effects of outliers on the performance of T2 control chart. In addition, the performance of the non-robust and the proposed robust control charts is evaluated in Phase II. Design/methodology/approach In this paper some, robust approaches including weighted maximum likelihood estimation, redescending M-estimator and a combination of these two approaches (WRM) are used to decrease the effects of outliers on estimating the logistic regression parameters as well as the performance of the T2 control chart. Findings The results of the simulation studies in both Phases I and II show the better performance of the proposed robust control charts rather than the non-robust control chart for estimating the logistic regression profile parameters and monitoring the logistic regression profiles. Practical implications In many practical applications, there are outliers in processes which may affect the estimation of parameters in Phase I and as a result of deteriorate the statistical performance of control charts in Phase II. The methods developed in this paper are effective for decreasing the effect of outliers in both Phases I and II. Originality/value This paper considers monitoring the logistic regression profile in Phase I under the presence of outliers. Also, three robust approaches are developed to decrease the effects of outliers on the parameter estimation and monitoring the logistic regression profiles in both Phases I and II.

scholarly journals X̅ and R control charts based on marshall-olkin inverse log-logistic distribution for positive skewed data

2020 ◽  
Vol 1 (1) ◽  
pp. 9-16
Author(s):  
O. L. Aako ◽  
J. A. Adewara ◽  
K. S Adekeye ◽  
E. B. Nkemnole
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Logistic Distribution ◽  
Skewed Data ◽  
Process Data ◽  
And Performance ◽  
Set Up ◽  
Control Limits ◽  
Normally Distributed

The fundamental assumption of variable control charts is that the data are normally distributed and spread randomly about the mean. Process data are not always normally distributed, hence there is need to set up appropriate control charts that gives accurate control limits to monitor processes that are skewed. In this study Shewhart-type control charts for monitoring positively skewed data that are assumed to be from Marshall-Olkin Inverse Loglogistic Distribution (MOILLD) was developed. Average Run Length (ARL) and Control Limits Interval (CLI) were adopted to assess the stability and performance of the MOILLD control chart. The results obtained were compared with Classical Shewhart (CS) and Skewness Correction (SC) control charts using the ARL and CLI. It was discovered that the control charts based on MOILLD performed better and are more stable compare to CS and SC control charts. It is therefore recommended that for positively skewed data, a Marshall-Olkin Inverse Loglogistic Distribution based control chart will be more appropriate.

Change point detection in social networks using a multivariate exponentially weighted moving average chart

2019 ◽  
Vol 46 (6) ◽  
pp. 790-809
Author(s):  
Ali Salmasnia ◽  
Mohammadreza Mohabbati ◽  
Mohammadreza Namdar
Keyword(s):  
Social Networks ◽  
Control Chart ◽  
Change Point ◽  
Moving Average ◽  
Change Point Detection ◽  
Network Density ◽  
Exponentially Weighted Moving Average ◽  
The Social ◽  
Point Detection ◽  
Weighted Moving Average

Although the significant role of social networks in communications between individuals has attracted researchers’ attention to the social networks, only few authors investigated social network monitoring in their studies. Most of the existing studies in this context suffer from the following three main drawbacks: (1) using the case-based network attributes such as person experiences and departments instead of the main attributes such as network density and centrality attributes, (2) monitoring the social attributes separately with the assumption that they are independent of each other and (3) ignoring detection of real time of change in the network. To overcome the above-mentioned disadvantages, this research develops a statistical method for monitoring the connections among actors in the social networks with the four most important network attributes consisting of (1) network density, (2) degree centrality, (3) betweenness centrality and (4) closeness centrality. To this end, a multivariate exponentially weighted moving average (MEWMA) control chart is used for simultaneous monitoring of these four correlated attributes. Furthermore, since the control chart usually does not alert a signal in the exact time of change due to type II error, this study presents a change point detection method to reduce cost and time required for diagnosing the control chart signal. Eventually, the efficiency of the proposed approach in comparison with the existing methods is evaluated through a simulation procedure. The results indicate that the suggested method has better performance than the univariate approach in detecting change point.

scholarly journals Effect of Phase I Estimation on Phase II Control Chart Performance with Profile Data

2014 ◽  
Vol 32 (1) ◽  
pp. 79-87 ◽  
Author(s):  
Yajuan Chen ◽  
Jeffrey B. Birch ◽  
William H. Woodall
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Profile Data

scholarly journals Change point detection in process control with robust individuals control chart

2021 ◽  
Vol 36 ◽  
pp. 01006
Author(s):  
Kooi Huat Ng ◽  
Kok Haur Ng ◽  
Jeng Young Liew
Keyword(s):  
Control Chart ◽  
Real Data ◽  
Change Point Detection ◽  
Absolute Deviation ◽  
Data Set ◽  
Data Collection Process ◽  
Process Standard Deviation ◽  
Exploratory Data ◽  
Point Detection ◽  
Better Than

It is crucial to realize when a process has changed and to what extent it has changed, then it would certainly ease the task. On occasion that practitioners could determine the time point of the change, they would have a smaller search window to pursue for the special cause. As a result, the special cause can be discovered quicker and the necessary actions to improve quality can be triggered sooner. In this paper, we had demonstrated the use of so-called exploratory data analysis robust modified individuals control chart incorporating the M-scale estimator and had made some comparisons to the existing charts. The proposed modified robust individuals control chart which incorporates the M-scale estimator in order to compute the process standard deviation offers substantial improvements over the existing median absolute deviation framework. With respect to the application in real data set, the proposed approach appears to perform better than the typical robust control chart, and outperforms other conventional charts particularly in the presence of contamination. Thus, it is for these reasons that the proposed modified robust individuals control chart is preferred especially when there is a possible existence of outliers in data collection process.

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