The Experts below are selected from a list of 405735 Experts worldwide ranked by ideXlab platform
Muhammad Riaz - One of the best experts on this subject based on the ideXlab platform.
-
on designing a new cumulative sum wilcoxon signed rank chart for Monitoring Process location
PLOS ONE, 2018Co-Authors: Muhammad Abid, Hafiz Zafar Nazir, Muhammad Tahir, Muhammad RiazAbstract:In this paper, ranked set sampling is used for developing a non-parametric location chart which is developed on the basis of Wilcoxon signed rank statistic. The average run length and some other characteristics of run length are used as the measures to assess the performance of the proposed scheme. Some selective distributions including Laplace (or double exponential), logistic, normal, contaminated normal and student’s t-distributions are considered to examine the performance of the proposed Wilcoxon signed rank control chart. It has been observed that the proposed scheme shows superior shift detection ability than some of the competing counterpart schemes covered in this study. Moreover, the proposed control chart is also implemented and illustrated with a real data set.
-
Using FIR to Improve CUSUM Charts for Monitoring Process Dispersion
Quality and Reliability Engineering International, 2016Co-Authors: Ridwan A. Sanusi, Muhammad Riaz, Nasir Abbas, Mu'azu Ramat AbujiyaAbstract:Statistical Process control deals with Monitoring Process to detect disturbances in the Process. These disturbances may be from the Process mean or variance. In this study, we propose some charts that are efficient for detecting early shifts in dispersion parameter, by applying the Fast Initial Response feature. Performance measures such as average run length, standard deviation of the run length, extra quadratic loss, relative average run length, and performance comparison index are used to compare the proposed charts with their existing counterparts, including the Shewhart R chart and the Shewhart S chart with and without warning lines. Others include the CUSUM R chart, the CUSUM S chart, the EWMA of ln S2, the CUSUM of ln S2, the Pσ CUSUM, the χ CUSUM, and the Change Point (CP) CUSUM charts. The proposed charts do not only detect early shifts in the Process dispersion faster, but also have better overall performance than their existing counterparts. Copyright © 2016 John Wiley & Sons, Ltd.
-
a new combined shewhart cumulative sum s chart for Monitoring Process standard deviation
Quality and Reliability Engineering International, 2016Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:The combined application of a Shewhart chart and cumulative sum (CUSUM) control chart is an effective tool for the detection of all sizes of Process shifts as the scheme combines the advantages of a CUSUM at detecting small to moderate shifts and Shewhart for the quick detection of very large shifts. This article proposes new combined Shewhart-CUSUM S charts based on the extreme variations of ranked set sampling technique, for efficient Monitoring of changes in the Process dispersion. Using Monte Carlo simulations, the combined scheme is designed to minimize the average extra quadratic loss over the entire Process shift domain. The results show that the combined Shewhart-CUSUM S charts uniformly outperform several other procedures for detecting increases and decreases in the Process variability. Moreover, the proposed scheme can detect changes that are small enough to escape the Shewhart S chart or fairly large to escape detection by the CUSUM S chart. Numerical example is given to illustrate the practical application of the proposed scheme using real industrial data.
-
combined application of shewhart and cumulative sum r chart for Monitoring Process dispersion
Quality and Reliability Engineering International, 2016Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:This study analyzes the performance of combined applications of the Shewhart and cumulative sum (CUSUM) range R chart and proposes modifications based on well-structured sampling techniques, the extreme variations of ranked set sampling, for efficient Monitoring of changes in the Process dispersion. In this combined scheme, the Shewhart feature enables quick detection of large shifts from the target standard deviation while the CUSUM feature takes care of small to moderate shifts from the target value. We evaluate the numerical performance of the proposed scheme in terms of the average run length, standard deviation of run length, the average ratio average run length, and average extra quadratic loss. The results show that the combined scheme can detect changes in the Process that were small or large enough to escape detection by the lone Shewhart R chart or CUSUM R chart, respectively. We present a comparison of the proposed schemes with several dispersion charts for Monitoring changes in Process variability. The practical application of the proposed scheme is demonstrated using real industrial data.
-
mixed cumulative sum exponentially weighted moving average control charts an efficient way of Monitoring Process location
Quality and Reliability Engineering International, 2015Co-Authors: Babar Zaman, Nasir Abbas, Muhammad Riaz, Ronald J. M. M. DoesAbstract:Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the Process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart, which is used to monitor the location of a Process. The performance of the proposed mixed CUSUM-EWMA control chart is measured through the average run length, extra quadratic loss, relative average run length, and a performance comparison index study. Comparisons are made with some existing charts from the literature. An example with real data is also given for practical considerations. Copyright © 2014 John Wiley & Sons, Ltd.
Michael B. C. Khoo - One of the best experts on this subject based on the ideXlab platform.
-
optimization designs and performance comparison of two cusum schemes for Monitoring Process shifts in mean and variance
European Journal of Operational Research, 2010Co-Authors: Zhang Wu, Michael B. C. Khoo, Mei Yang, Fongjung YuAbstract:In statistical Process control (SPC), when dealing with a quality characteristic x that is a variable, it is usually necessary to monitor both the mean value and variability. This article proposes an optimization algorithm (called the holistic algorithm) to design the CUSUM charts for this purpose. It facilitates the determination of the charting parameters of the CUSUM charts and considerably or significantly increases their overall detection effectiveness. A single CUSUM chart (called the ABS CUSUM chart) has been developed by the holistic algorithm and fully investigated. This chart is able to detect two-sided mean shifts and increasing variance shifts by inspecting the absolute value of sample mean shift. The results of performance studies show that the overall performance of the ABS CUSUM chart is nearly as good as an optimal 3-CUSUM scheme (a scheme incorporating three individual CUSUM charts). However, since the ABS CUSUM chart is easier for implementation and design, it may be more suitable for many SPC applications in which both mean and variance of a variable have to be monitored.
-
A modified S chart for the Process variance
Quality Engineering, 2005Co-Authors: Michael B. C. KhooAbstract:[This abstract is based on the author's abstract.]When Monitoring Process dispersion, quality control practitioners favor the R chart for small sample sizes. The S chart is the preferred choice for larger sample sizes because it is slightly more effect..
Mu'azu Ramat Abujiya - One of the best experts on this subject based on the ideXlab platform.
-
Using FIR to Improve CUSUM Charts for Monitoring Process Dispersion
Quality and Reliability Engineering International, 2016Co-Authors: Ridwan A. Sanusi, Muhammad Riaz, Nasir Abbas, Mu'azu Ramat AbujiyaAbstract:Statistical Process control deals with Monitoring Process to detect disturbances in the Process. These disturbances may be from the Process mean or variance. In this study, we propose some charts that are efficient for detecting early shifts in dispersion parameter, by applying the Fast Initial Response feature. Performance measures such as average run length, standard deviation of the run length, extra quadratic loss, relative average run length, and performance comparison index are used to compare the proposed charts with their existing counterparts, including the Shewhart R chart and the Shewhart S chart with and without warning lines. Others include the CUSUM R chart, the CUSUM S chart, the EWMA of ln S2, the CUSUM of ln S2, the Pσ CUSUM, the χ CUSUM, and the Change Point (CP) CUSUM charts. The proposed charts do not only detect early shifts in the Process dispersion faster, but also have better overall performance than their existing counterparts. Copyright © 2016 John Wiley & Sons, Ltd.
-
a new combined shewhart cumulative sum s chart for Monitoring Process standard deviation
Quality and Reliability Engineering International, 2016Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:The combined application of a Shewhart chart and cumulative sum (CUSUM) control chart is an effective tool for the detection of all sizes of Process shifts as the scheme combines the advantages of a CUSUM at detecting small to moderate shifts and Shewhart for the quick detection of very large shifts. This article proposes new combined Shewhart-CUSUM S charts based on the extreme variations of ranked set sampling technique, for efficient Monitoring of changes in the Process dispersion. Using Monte Carlo simulations, the combined scheme is designed to minimize the average extra quadratic loss over the entire Process shift domain. The results show that the combined Shewhart-CUSUM S charts uniformly outperform several other procedures for detecting increases and decreases in the Process variability. Moreover, the proposed scheme can detect changes that are small enough to escape the Shewhart S chart or fairly large to escape detection by the CUSUM S chart. Numerical example is given to illustrate the practical application of the proposed scheme using real industrial data.
-
combined application of shewhart and cumulative sum r chart for Monitoring Process dispersion
Quality and Reliability Engineering International, 2016Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:This study analyzes the performance of combined applications of the Shewhart and cumulative sum (CUSUM) range R chart and proposes modifications based on well-structured sampling techniques, the extreme variations of ranked set sampling, for efficient Monitoring of changes in the Process dispersion. In this combined scheme, the Shewhart feature enables quick detection of large shifts from the target standard deviation while the CUSUM feature takes care of small to moderate shifts from the target value. We evaluate the numerical performance of the proposed scheme in terms of the average run length, standard deviation of run length, the average ratio average run length, and average extra quadratic loss. The results show that the combined scheme can detect changes in the Process that were small or large enough to escape detection by the lone Shewhart R chart or CUSUM R chart, respectively. We present a comparison of the proposed schemes with several dispersion charts for Monitoring changes in Process variability. The practical application of the proposed scheme is demonstrated using real industrial data.
-
enhanced cumulative sum charts for Monitoring Process dispersion
PLOS ONE, 2015Co-Authors: Mu'azu Ramat Abujiya, Muhammad RiazAbstract:The cumulative sum (CUSUM) control chart is widely used in industry for the detection of small and moderate shifts in Process location and dispersion. For efficient Monitoring of Process variability, we present several CUSUM control charts for Monitoring changes in standard deviation of a normal Process. The newly developed control charts based on well-structured sampling techniques - extreme ranked set sampling, extreme double ranked set sampling and double extreme ranked set sampling, have significantly enhanced CUSUM chart ability to detect a wide range of shifts in Process variability. The relative performances of the proposed CUSUM scale charts are evaluated in terms of the average run length (ARL) and standard deviation of run length, for point shift in variability. Moreover, for overall performance, we implore the use of the average ratio ARL and average extra quadratic loss. A comparison of the proposed CUSUM control charts with the classical CUSUM R chart, the classical CUSUM S chart, the fast initial response (FIR) CUSUM R chart, the FIR CUSUM S chart, the ranked set sampling (RSS) based CUSUM R chart and the RSS based CUSUM S chart, among others, are presented. An illustrative example using real dataset is given to demonstrate the practicability of the application of the proposed schemes.
H.-j. Huang - One of the best experts on this subject based on the ideXlab platform.
-
variable sampling interval synthetic control charts for jointly Monitoring Process mean and standard deviation
International Journal of Industrial Engineering-theory Applications and Practice, 2006Co-Authors: F L Che, H.-j. HuangAbstract:This paper proposes a synthetic control chart for jointly Monitoring the shifts in the mean and/or standard deviation of a normally distributed Process. The synthetic chart is a combination of the Max chart and the conforming run length (eRL) chart. The Max chart can be treated as a special case of the synthetic chart. The operation, design, and performance of this control chart are described. Comparisons of the average run length (ARL) performance of the synthetic chart, the Max chart and the traditional joint X and S charts indicate that the synthetic chart outperforms the other two charts. The variable sampling interval (VSI) schemes as an enha.ncement to the synthetic chart are discussed to further improve the chart performance. A numerical example is given to illustrate the application of synthetic chart and its VSI scheme.
-
A synthetic control chart for Monitoring Process dispersion with sample range
The International Journal of Advanced Manufacturing Technology, 2005Co-Authors: F. L. Chen, H.-j. HuangAbstract:A synthetic control chart for Monitoring the changes in the standard deviation of a normally distributed Process is proposed in this paper. The synthetic chart consists of the sample range (R) chart and the conforming run-length (CRL) chart. The R chart can be viewed as a special case of the synthetic chart. The operation, design and performance of this chart are described. Average run- length comparisons between other procedures and the synthetic chart are presented. It indicates that the synthetic chart is a good alternative for Monitoring Process dispersion. The variable sampling interval (VSI) schemes, as an enhancement to the synthetic chart, are discussed to further improve the chart performance. An example is presented to illustrate the application of synthetic chart and its VSI scheme.
-
A synthetic control chart for Monitoring Process dispersion with sample range
The International Journal of Advanced Manufacturing Technology, 2005Co-Authors: F. L. Chen, H.-j. HuangAbstract:[[abstract]]A synthetic control chart for Monitoring the changes in the standard deviation of a normally distributed Process is proposed in this paper. The synthetic chart consists of the sample range (R) chart and the conforming run-length (CRL) chart. The R chart can be viewed as a special case of the synthetic chart. The operation, design and performance of this chart are described. Average run- length comparisons between other procedures and the synthetic chart are presented. It indicates that the synthetic chart is a good alternative for Monitoring Process dispersion. The variable sampling interval (VSI) schemes, as an enhancement to the synthetic chart, are discussed to further improve the chart performance. An example is presented to illustrate the application of synthetic chart and its VSI scheme.[[fileno]]2020408010005[[department]]工工
-
a synthetic control chart for Monitoring Process dispersion with sample standard deviation
Computers & Industrial Engineering, 2005Co-Authors: H.-j. Huang, F L CheAbstract:Some factors in manufacturing such as faulty raw material, unskilled/careless operators, and loosening of machine settings may lead to a change in Process dispersion without necessarily influencing the level of the Process mean. This paper proposes a synthetic control chart for Monitoring the changes in the standard deviation of a normally distributed Process. The synthetic chart is a combination of the sample standard deviation (S) chart and the conforming run length (CRL) chart. The S chart can be regarded as a special case of the synthetic chart. The operation, design, and performance of this chart are described. Average run length comparisons between other procedures and the synthetic chart are presented. It indicates that the synthetic chart is a good alternative for Monitoring Process dispersion. The variable sampling interval (VSI) schemes as an enhancement to the synthetic chart are discussed to further improve the chart performance. An example is presented to illustrate the application of synthetic chart and its VSI scheme.
Zhang Wu - One of the best experts on this subject based on the ideXlab platform.
-
optimization designs and performance comparison of two cusum schemes for Monitoring Process shifts in mean and variance
European Journal of Operational Research, 2010Co-Authors: Zhang Wu, Michael B. C. Khoo, Mei Yang, Fongjung YuAbstract:In statistical Process control (SPC), when dealing with a quality characteristic x that is a variable, it is usually necessary to monitor both the mean value and variability. This article proposes an optimization algorithm (called the holistic algorithm) to design the CUSUM charts for this purpose. It facilitates the determination of the charting parameters of the CUSUM charts and considerably or significantly increases their overall detection effectiveness. A single CUSUM chart (called the ABS CUSUM chart) has been developed by the holistic algorithm and fully investigated. This chart is able to detect two-sided mean shifts and increasing variance shifts by inspecting the absolute value of sample mean shift. The results of performance studies show that the overall performance of the ABS CUSUM chart is nearly as good as an optimal 3-CUSUM scheme (a scheme incorporating three individual CUSUM charts). However, since the ABS CUSUM chart is easier for implementation and design, it may be more suitable for many SPC applications in which both mean and variance of a variable have to be monitored.
-
a control chart for Monitoring Process mean based on attribute inspection
International Journal of Production Research, 2008Co-Authors: Zhang Wu, Jianxin JiaoAbstract:This article proposes a new control chart, namely the MON chart, which employs attribute inspection (inspecting whether units are conforming or nonconforming) to monitor the mean value of a variable characteristic x. A unit is classified as nonconforming if the value of x falls beyond a fixed warning limit. A sample is regarded as suspect if more than m out of n units (referred to as MON) in the sample are nonconforming. A MON chart produces an out-of-control signal when the interval between two suspect samples is smaller than a control limit. The MON chart is distinctively advantageous owing to its simplicity in implementation. In particular, the MON chart uses attribute inspection and eliminates the need for any computation. In addition, the MON chart makes use of information not only about the magnitude of x, but also the interval between two suspect samples. Therefore, it always outperforms the X chart and often excels the CUSUM chart on the basis of same inspection cost. Furthermore, the MON chart pe...
