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James C Benneyan - One of the best experts on this subject based on the ideXlab platform.

  • use and interpretation of statistical quality Control Charts
    International Journal for Quality in Health Care, 1998
    Co-Authors: James C Benneyan
    Abstract:

    Originally developed at Bell Laboratories by Dr Walter Shewhart [1] in 1924 specifically to help detect statistical changes in process quality, Control Charts have since become one of several primary tools of quality Control and process improvement. Quality Control Charts are chronological graphs of process data that, although based in statistical theory, are easy for practitioners to use and interpret. These Charts also can help users to develop an understanding of the performance of a process and to evaluate any benefits or consequences of process interventions, complementing traditional methods by providing additional longitudinal information that otherwise might not be detected [2]. This article provides a brief introduction to the use of statistical quality Control Charts for analyzing, monitoring, and improving health care processes. After an overview of key concepts, several examples illustrate Control chart use and interpretation. The article concludes with some common pitfalls to avoid and references for further exploration. Readers unfamiliar with the topic should also read introductory materials on the principles of quality management and the philosophies of the late quality pioneer, Dr W. E. Deming [3].

Subha Chakraborti - One of the best experts on this subject based on the ideXlab platform.

  • nonparametric distribution free Control Charts an updated overview and some results
    Quality Engineering, 2019
    Co-Authors: Subha Chakraborti, Marien Alet Graham
    Abstract:

    Control Charts that are based on assumption(s) of a specific form for the underlying process distribution are referred to as parametric Control Charts. There are many applications where there is in...

  • nonparametric distribution free quality Control Charts
    Wiley StatsRef: Statistics Reference Online, 2011
    Co-Authors: Subha Chakraborti
    Abstract:

    Control Charts that are typically based on the assumption of a specific form of a parametric distribution, such as the normal, are called parametric Control Charts. In many applications, however, there is not enough information to justify this assumption and Control Charts that do not depend on a particular distributional assumption are desirable. Nonparametric or distribution-free Control Charts can serve this broader purpose. A key advantage of nonparametric Charts is that its in-Control run length distribution is the same for all continuous process distributions. This means, for example, that the false alarm rate and the in-Control average run length of a nonparametric chart is the same for all continuous distributions. This is not true for parametric Control Charts in general and consequently their in-Control robustness can be a legitimate concern. Nonparametric Charts are often more robust and efficient than their normal theory counterparts under heavy-tailed and/or asymmetric distributions. In this paper we discuss developments, mostly in the area of univariate nonparametric Control Charts; this includes cases when the underlying parameters are specified as well as when they are unknown and are therefore estimated from the data. The majority of the Charts are for monitoring the location; few Charts are available for scale. Keywords: Distribution-free; Robust; Location-scale; Sign; Rank; Precedence; Run length; Median; Shewhart chart; CUSUM chart; EWMA chart; Mann-Whitney-Wilcoxon; Phase I; Phase II

  • nonparametric Control Charts an overview and some results
    Journal of Quality Technology, 2001
    Co-Authors: Subha Chakraborti, Van Der Paul P Laan, Saad T Bakir
    Abstract:

    The literature on nonparametric or distribution-free Control Charts for univariate variables data is examined. The advantages of these Charts have over more traditional distribution-based Control Charts are demonstrated. Constructive criticism of the li..

Francis Pelletier - One of the best experts on this subject based on the ideXlab platform.

  • power curve monitoring using weighted moving average Control Charts
    Renewable Energy, 2016
    Co-Authors: Philippe Cambron, R Lepvrier, Christian Masson, Antoine Tahan, Francis Pelletier
    Abstract:

    Abstract A method for the monitoring of a wind turbine generator is proposed, based on its power curve and using Control Charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) Control Charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference power curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA Control Charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method's robustness.

Philippe Cambron - One of the best experts on this subject based on the ideXlab platform.

  • power curve monitoring using weighted moving average Control Charts
    Renewable Energy, 2016
    Co-Authors: Philippe Cambron, R Lepvrier, Christian Masson, Antoine Tahan, Francis Pelletier
    Abstract:

    Abstract A method for the monitoring of a wind turbine generator is proposed, based on its power curve and using Control Charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) Control Charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference power curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA Control Charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method's robustness.

Muhammad Riaz - One of the best experts on this subject based on the ideXlab platform.

  • Progressive Variance Control Charts for Monitoring Process Dispersion
    Communications in Statistics - Theory and Methods, 2014
    Co-Authors: Raja Fawad Zafar, Nasir Abbas, Muhammad Riaz, Zawar Hussain
    Abstract:

    In a process, the deviation from location or scale parameters affects the quality of the process and waste resources. So it is essential to monitor such processes for possible changes due to any assignable causes. Control Charts are the most famous tool used to meet this intention. It is useless to monitor process location until the assurance that process dispersion is in-Control. This study proposes some new two-sided memory Control Charts named as progressive variance (PV) Control Charts which are based on sample variance to monitor changes in process dispersion assuming normality of quality characteristic to be monitored. Simulation studies are made, and an example is discussed to evaluate the performance of the proposed Charts. The comparison of the proposed chart is made with exponentially weighted moving average- and cumulative sum-type Charts for process dispersion. The study shows that performance of the proposed Charts are uniformly better than its competitors for detecting positive shifts while ...

  • improving the performance of exponentially weighted moving average Control Charts
    Quality and Reliability Engineering International, 2014
    Co-Authors: Muazu Ramat Abujiya, Muhammad Riaz, Muhammad Hisyam Lee
    Abstract:

    A Control chart is a graphical tool used for monitoring a production process and quality improvement. One such charting procedure is the Shewhart-type Control chart, which is sensitive mainly to the large shifts. For small shifts, the cumulative sum (CUSUM) Control Charts and exponentially weighted moving average (EWMA) Control Charts were proposed. To further enhance the ability of the EWMA Control chart to quickly detect wide range process changes, we have developed an EWMA Control chart using the median ranked set sampling (RSS), median double RSS and the double median RSS. The findings show that the proposed median-ranked sampling procedures substantially increase the sensitivities of EWMA Control Charts. The newly developed Control Charts dominate most of their existing counterparts, in terms of the run-length properties, the Average Extra Quadratic Loss and the Performance Comparison Index. These include the classical EWMA, fast initial response EWMA, double and triple EWMA, runs-rules EWMA, the max EWMA with mean-squared deviation, the mixed EWMA-CUSUM, the hybrid EWMA and the combined Shewhart–EWMA based on ranks. An application of the proposed schemes on real data sets is also given to illustrate the implementation and procedural details of the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

  • An application of Control Charts in manufacturing industry
    Journal of Statistical and Econometric Methods, 2012
    Co-Authors: Muhammad Riaz, Faqir Muhammad
    Abstract:

    The range Control chart and the X bar Control chart are the well known and the most popular tools for detecting out- of-Control signals in the Statistical Quality Control (SQC). The Control Charts has shown his worth in the manufacturing industry. In this study we have applied the range and the X bar Control Charts to a product of Swat Pharmaceutical Company. The variables under study were weight/ml, Ph, Citrate % and the amount of fill. Besides the X bar Control chart, the exponentially weighted moving average Control chart and the multivariate Hotelling’s T 2 Control chart were applied to the same data.