A mixed nonparametric control chart for efficient process monitoring

Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart.

Section: Performance Evaluation and Comparisons Of The Proposed Chartmentioning

confidence: 99%

On designing an efficient control chart to monitor fraction nonconforming

Abbas

Nazir

Akhtar

et al. 2019

“…Abbasi et al 30 proposed the MEC-SN control chart by combining the features of the EWMA and CUSUM control charts. Without loss of generality, suppose that…”

Section: The Mec-sn Control Chartmentioning

confidence: 99%

“…, and the control chart triggers an OOC signal if either C − t or C þ t exceeds H t . For more details, see Abbasi et al 30…”

Section: The Mec-sn Control Chartmentioning

confidence: 99%

“…They showed that these nonparametric control charts are appropriate and capable of detecting small shifts in the process location. Recently, Abbasi et al 30 have suggested the mixed EWMA-CUSUM sign chart (MEC-SN) using arcsine transformation and showed that it has better shift detection ability when compared with its other counterparts. Later, Lu 27 developed a nonparametric double generally weighted moving average sign control chart for monitoring small deviations from the target value by integrating the virtue of GWMA and DEWMA control charts.…”

Section: Introductionmentioning

confidence: 99%

“…Following Yang et al 25 and Zhang and Chen, 28 a nonparametric double EWMA control chart on the basis of the sign statistic (hereafter, named as DEWMA-SN) under arcsine transformation was suggested by Riaz and Abbasi, 29 but they only considered the case of λ 1 = λ 2 . Recently, Abbasi et al 30 have suggested the mixed EWMA-CUSUM sign chart (MEC-SN) using arcsine transformation and showed that it has better shift detection ability when compared with its other counterparts. Haq 31 proposed the synthetic version of EWMA sign control chart by integrating existing EWMA sign control chart with the conforming run length chart.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new nonparametric double exponentially weighted moving average control chart

Raza

Nawaz

Aslam

et al. 2019

Self Cite

There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.

A hybrid homogeneously weighted moving average control chart for process monitoring

Adeoti

Koleoso

2020

Several modifications and enhancements to control charts in increasing the performance of small and moderate process shifts have been introduced in the quality control charting techniques. In this paper, a new hybrid control chart for monitoring process location is proposed by combining two homogeneously weighted moving average (HWMA) control charts. The hybrid homogeneously weighted moving average (HHWMA) statistic is derived using two smoothing constants λ 1 and λ 2. The average run length (ARL) and the standard deviation of the run length (SDRL) values of the HHWMA control chart are obtained and compared with some existing control charts for monitoring small and moderate shifts in the process location. The results of study show that the HHWMA control chart outperforms the existing control charts in many situations. The application of the HHWMA chart is demonstrated using a simulated data. K E Y W O R D S average run length (ARL), control chart, homogeneously weighted moving average (HWMA), hybrid homogeneously weighted moving average (HHWMA), process monitoring