A Directional Multivariate Control Chart for Monitoring Univariate Autocorrelated Processes

2012 ◽  
Vol 217-219 ◽  
pp. 2607-2613
Author(s):  
Wen Wan Yang ◽  
Xue Min Zi ◽  
Chang Liang Zou
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Sign Test ◽  
Directional Information ◽  
Autocorrelated Process ◽  
Autocorrelated Processes ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Multivariate Control ◽  
Weighted Moving Average

A new nonparametric multivariate control chart, based on a spatial-sign test and integrating the directional information from processes with the exponentially weighted moving average (EWMA) scheme, is developed for monitoring the mean of a univariate autocorrelated process. Simulation studies show that it has robustness in in-control (IC) performance, and it is more sensitive to the small and moderate mean shifts for non-normality underlying process than other existing multivariate chart methods.

Progressive mean exponentially weighted moving average control chart for monitoring the process location

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nurudeen Ayobami Ajadi ◽  
Osebekwin Asiribo ◽  
Ganiyu Dawodu
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Performance Measure ◽  
Exponentially Weighted Moving Average ◽  
Average Scheme ◽  
Content Type ◽  
The Mean ◽  
Memory Type ◽  
Weighted Moving Average ◽  
Exponentially Weighted

PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

The Detection Method of Small Shifts Based on Stationary Autocorrelated Process

2012 ◽  
Vol 235 ◽  
pp. 227-232
Author(s):  
Hai Yu Wang
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Independent Random Variables ◽  
Independence Assumption ◽  
Autocorrelated Process ◽  
Autocorrelated Processes ◽  
Moving Average Model ◽  
Auto Regressive ◽  
Mean And Variance ◽  
Weighted Moving Average

When control charts are used to monitor a process, a standard assumption is that observations from the process at different times are independent random variables. However, the independence assumption is often not reasonable for processes of interest in many applications because the dynamics of the process product autocorrelation in the process observations. The presence of significant autocorrelation in the process observations can have a large impact on traditional control charts developed under the independence assumption. A method of monitoring little shifts in stationary autocorrelated process is discussed in this paper. At first, auto-regressive moving-average model is used to fit stationary autocorrelated process. Then, process autocorrelation can be removed by residual method, and exponentially weighted moving average charts are constructed to monitor little shifts of process mean and variance. Comparing with other methods, we can illustration that this EWMA residuals charts have better efficiency for stationary autocorrelated processes.

scholarly journals On Efficient Monitoring of Weibull Lifetimes Using Censored Median Hybrid DEWMA Chart

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Syed Muhammad Muslim Raza ◽  
Sajid Ali ◽  
Ismail Shah ◽  
Lichen Wang ◽  
Zhen Yue
Keyword(s):  
Censored Data ◽  
Control Chart ◽  
Scale Parameter ◽  
Average Run Length ◽  
Moving Average ◽  
Type I ◽  
Conditional Median ◽  
The Mean ◽  
Censored Observations ◽  
Weighted Moving Average

A control chart named as the hybrid double exponentially weighted moving average (HDEWMA) to monitor the mean of Weibull distribution in the presence of type-I censored data is proposed in this study. In particular, the focus of this study is to use the conditional median (CM) for the imputation of censored observations. The control chart performance is assessed by the average run length (ARL). A comparison between CM-DEWMA control chart and CM-based HDEWMA control chart is also presented in this article. Assuming different shift sizes and censoring rates, it is observed that the proposed control chart outperforms the CM-DEWMA chart. The effect of estimation, particularly the scale parameter estimation, on ARL is also a part of this study. Finally, a practical example is provided to understand the application and to investigate the performance of the proposal in practical scenarios.

A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD

2008 ◽  
Vol 15 (03) ◽  
pp. 217-245 ◽  
Author(s):  
MICHAEL B. C. KHOO ◽  
ZHANG WU ◽  
ABDU M. A. ATTA
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Synthetic Control ◽  
Exponentially Weighted Moving Average ◽  
Process Mean ◽  
Variance Method ◽  
The Mean ◽  
Synthetic Chart ◽  
Weighted Moving Average ◽  
Exponentially Weighted

A synthetic control chart for detecting shifts in the process mean integrates the Shewhart [Formula: see text] chart and the conforming run length chart. It is known to outperform the Shewhart [Formula: see text] chart for all magnitudes of shifts and is also superior to the exponentially weighted moving average chart and the joint [Formula: see text]-exponentially weighted moving average charts for shifts of greater than 0.8σ in the mean. A synthetic chart for the mean assumes that the underlying process follows a normal distribution. In many real situations, the normality assumption may not hold. This paper proposes a synthetic control chart to monitor the process mean of skewed populations. The proposed synthetic chart uses a method based on a weighted variance approach of setting up the control limits of the [Formula: see text] sub-chart for skewed populations when process parameters are known and unknown. For symmetric populations, however, the limits of the new [Formula: see text] sub-chart are equivalent to that of the existing [Formula: see text] sub-chart which assumes a normal underlying distribution. The proposed synthetic chart based on the weighted variance method is compared by Monte Carlo simulation with many existing control charts for skewed populations when the underlying populations are Weibull, lognormal, gamma and normal and it is generally shown to give the most favourable results in terms of false alarm and mean shift detection rates.

scholarly journals An EWMA control chart for the mean of individual streams in multiple stream processes

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Bruno Francisco Teixeira Simões ◽  
Eugenio Kahn Epprecht
Keyword(s):  
Control Chart ◽  
Individual Component ◽  
Moving Average ◽  
Optimal Designs ◽  
Additional Contribution ◽  
Statistical Control ◽  
Ewma Control Chart ◽  
The Mean ◽  
The Individual ◽  
Weighted Moving Average

Abstract: In a multiple stream process (MSP) a product is manufactured in a number of streams in parallel. The traditional tool for monitoring MSPs, the group control chart (GCC), does not take into account that typically the value of the quality variable in each stream is the sum of a component common to all streams and an individual component, of the particular stream. This may render the GCC ineffective in detecting shifts in the mean of individual streams. Based on this two-components model, we propose an exponentially weighted moving average (EWMA) GCC to monitor the means of the individual streams components. We optimize its design (minimizing the ARL for given shifts in the mean of a stream) and compare their ARLs with the ones of other existing charts devised for two-components MSPs. For this comparison, we needed to obtain optimal designs of these previous charts too, which were not available in the literature; this is an additional contribution of our work. The ARLs of the charts were obtained by simulation, with a number of runs sufficiently large to ensure precise results. The results show that the proposed chart outperforms the previous ones, becoming thus recommended for the statistical control of MSPs.

scholarly journals Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qinkai Han ◽  
Zhentang Wang ◽  
Tao Hu
Keyword(s):  
Control Chart ◽  
Condition Monitoring ◽  
Control Charts ◽  
Moving Average ◽  
Monitoring Method ◽  
Abnormal State ◽  
Exponential Weighted Moving Average ◽  
Multivariate Control ◽  
Mewma Control Chart ◽  
Weighted Moving Average

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

Distribution-Free Multivariate Control Chart Based on Change-Point Model

2014 ◽  
Vol 971-973 ◽  
pp. 1435-1439
Author(s):  
Mei Hua Duan ◽  
Xue Min Zi
Keyword(s):  
Control Chart ◽  
Change Point ◽  
Point Model ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Distribution Free ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Change Point Model ◽  
Multivariate Control

It is common to monitor several quality characteristics of a process simultaneously in modern quality control, and it is called multivariate statistical process control (MSPC) in the literature. A change-point control chart for detecting shifts in the mean of a multivariate statistical process is developed for the case where the nominal value of the mean is unknown but some historical samples are available. This control chart is called distribution-free multivariate control chart based on change-point model. Its distribution robustness is a significant advantage where we usually know nothing about the underlying distribution. And the simulated results show that this approach has a good performance across the range of possible shifts.

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