A New Nonparametric Composite Exponentially Weighted Moving Average Sign Control Chart

Raza, Muhammad Ali; Aslam, Muhammad; Farooq, Muhammad; Ahmad, Tanvir; Sherwani, Rehan Ahmad Khan; Bhatti, Sajjad Haider

doi:10.24200/sci.2020.53032.3022

Cited by 1 publication

(1 citation statement)

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“…Alevizakos et al [15] proposed a triple exponentially weighted moving average control chart by combining the three EWMA statistics to detect small process shifts in the location parameter. For more details regarding the mixed control charting schemes, see Rasheed et al [16], Engmann and Han [17], Raza et al [18], and Han et al [19].…”

Section: Introductionmentioning

confidence: 99%

Mixed Exponentially Weighted Moving Average—Moving Average Control Chart with Application to Combined Cycle Power Plant

et al. 2023

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Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality of the products or services. In the literature, a mixed exponentially weighted moving average–moving average (EWMA–MA) control chart for monitoring the process location is proposed to enhance the overall shift detection ability of the EWMA control chart. It is noted that the moving averages terms were considered as independent irrespective of their order. Consequently, the covariance terms are ignored while deriving the variance expression of the monitoring statistic. However, the successive moving averages of span w might not be independent since each term includes w − 1 preceding samples’ information. In this study, the variance expression of the mixed EWMA-MA charting statistic is derived by considering the dependency among the sequential moving averages. The control limits of the mixed EWMA-MA control chart are revised and the run-length profile is studied by using Monte Carlo simulations. The performance of the mixed EWMA-MA chart is compared with the existing counterparts and its robustness under various process distributions is studied. In the end, a real-life example is provided to demonstrate its application by using the data from a combined cycle power plant.

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