An algorithmic approach to outlier detection and parameter estimation in Phase I for designing Phase II EWMA control chart

2020 ◽  
Vol 144 ◽  
pp. 106440
Author(s):  
Murat Caner Testik ◽  
Ozge Kara ◽  
Sven Knoth
Keyword(s):  
Parameter Estimation ◽  
Phase I ◽  
Outlier Detection ◽  
Control Chart ◽  
Phase Ii ◽  
Algorithmic Approach ◽  
Ewma Control Chart

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 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 On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 706 ◽  
Author(s):  
Shahid Hussain ◽  
Sun Mei ◽  
Muhammad Riaz ◽  
Saddam Akber Abbasi
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
Auxiliary Information ◽  
Location Parameter ◽  
Attractive Alternative ◽  
Location Parameters ◽  
The Mean ◽  
Two Phases

A control chart is often used to monitor the industrial or services processes to improve the quality of the products. Mostly, the monitoring of location parameters, both in Phase I and Phase II, is done using a mean control chart with the assumption that the process is free from outliers or the estimators are correctly estimated from in-control samples. Generally, there are question marks about such kind of narratives. The performance of the mean chart is highly affected in the presence of outliers. Therefore, the median chart is an attractive alternative to the mean chart in this situation. The control charts are usually implemented in two phases: Phase I (retrospective) and Phase II (prospective/monitoring). The efficiency of any control chart in Phase II depends on the accuracy of control limits obtained from Phase I. The current study focuses on the Phase I analysis of location parameters using median control charts. We examined the performance of different auxiliary information-based median control charts and compared the results with the usual median chart. Standardized variance and relative efficacy are used as performance measures to evaluate the efficiency of median estimators. Moreover, the probability to signal measure is used to evaluate the performance of proposed control charts to detect any potential changes in the process. The results revealed that the proposed auxiliary information based median control charts perform better in Phase I analysis. In addition, a practical illustration of an industrial scenario demonstrated the significance of the proposed control charts, in which the monitoring of concrete compressive strength is emphasized.

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.

Guidelines for automating Phase I of control charts by considering effects on Phase-II performance of individuals control chart

2019 ◽  
Vol 32 (2) ◽  
pp. 223-243 ◽  
Author(s):  
Murat Atalay ◽  
Murat Caner Testik ◽  
Serhan Duran ◽  
Christian H. Weiß
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Phase Ii ◽  
Control Charts

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bin Nie ◽  
Diqing Liu ◽  
Xiaohui Liu ◽  
Wenjing Ye
Keyword(s):  
Phase I ◽  
Outlier Detection ◽  
Control Chart ◽  
Real World ◽  
Restrictive Assumption ◽  
Content Type ◽  
Linear Profile ◽  
Linear Profiles ◽  
Non Linear ◽  
Modified Hausdorff Distance

PurposeThe purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.Design/methodology/approachThe proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.FindingsThe simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.Practical implicationsThe research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.Originality/valueDue to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

scholarly journals On the Use of the K-Chart for Phase II Monitoring of Simple Linear Profiles

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Walid Gani ◽  
Mohamed Limam
Keyword(s):  
Control Chart ◽  
Phase Ii ◽  
Control Charts ◽  
High Sensitivity ◽  
Small Shift ◽  
Explanatory Variables ◽  
Ewma Control Chart ◽  
Linear Profiles ◽  
Kernel Distance ◽  
Multivariate Control

Control charts for monitoring linear profiles are used to control quality processes which are characterized by a relationship between a response variable and one or more explanatory variables. In the literature, the majority of control charts deal with phase II analysis of linear profiles, where the objective is to assess the performance of control charts in detecting shifts in the parameters of linear profiles. Recently, the kernel distance-based multivariate control chart, also known as the K-chart, has received much attention as a promising nonparametric control chart with high sensitivity to small shifts in the process. Despite its numerous advantages, no work has proposed the use of the K-chart for monitoring simple linear profiles and that serves the motivation for this paper. This paper proposes the use of the K-chart for monitoring simple linear profiles. A benchmark example is used to show the construction methodology of the K-chart for simultaneously monitoring the slope and intercept of linear profile. In addition, performance of the K-chart in detecting out-of-control profiles is assessed and compared with traditional control charts. Results demonstrate that the K-chart performs better than the T2 control chart, EWMA control chart, and R-chart under small shift in the slope.

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