Abstract:Abstract-The objective of this paper is to study Statistical Process Control (SPC) with a Moving Average control chart (MA) for monitoring the non-conforming product. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. The ARL should be sufficiently large while the process is still in-control and the Average Delay time (AD) (mean delay of true alarm times) should be small when the process goes out-of-control. The explici… Show more
“…Wong et al 2 discussed the sensitivity power of the MA chart. Khoo and Wong 3 and Areepong 4 proposed double MA control charts. Mohsin et al 5 worked on a weighted MA chart using loss function.…”
mentioning
confidence: 99%
“…The values of NARL when n ǫ[4,6] and w ǫ[3,5]. , 211.83] [199.03, 205.38] [297.65, 303.3] [289.36, 290.94] [369.7, 372.87] [349.46, 353.55] 0.1 [138.75, 106.01] [137.11, 102.25] [197.63, 140.91] [196.55, 137.01] [248.96, 173.35] [241.98, 171.21]…”
the existing moving average control charts can be only applied when all observations in the data are determined, precise, and certain. But, in practice, the data from the weather monitoring is not exact and express in the interval. in this situation, the available monitoring plans cannot be applied for the monitoring of weather data. A new moving average control chart for the normal distribution is offered under the neutrosophic statistics. The parameters of the offered chart are determined through simulation under neutrosophic statistics. the comparison study shows the superiority of the proposed chart over the moving average control chart under classical statistics. A real example from the weather is chosen to present the implementation of the chart. from the simulation study and real data, the proposed chart is found to be effective to be applied for temperature monitoring than the existing control chart.
“…Wong et al 2 discussed the sensitivity power of the MA chart. Khoo and Wong 3 and Areepong 4 proposed double MA control charts. Mohsin et al 5 worked on a weighted MA chart using loss function.…”
mentioning
confidence: 99%
“…The values of NARL when n ǫ[4,6] and w ǫ[3,5]. , 211.83] [199.03, 205.38] [297.65, 303.3] [289.36, 290.94] [369.7, 372.87] [349.46, 353.55] 0.1 [138.75, 106.01] [137.11, 102.25] [197.63, 140.91] [196.55, 137.01] [248.96, 173.35] [241.98, 171.21]…”
the existing moving average control charts can be only applied when all observations in the data are determined, precise, and certain. But, in practice, the data from the weather monitoring is not exact and express in the interval. in this situation, the available monitoring plans cannot be applied for the monitoring of weather data. A new moving average control chart for the normal distribution is offered under the neutrosophic statistics. The parameters of the offered chart are determined through simulation under neutrosophic statistics. the comparison study shows the superiority of the proposed chart over the moving average control chart under classical statistics. A real example from the weather is chosen to present the implementation of the chart. from the simulation study and real data, the proposed chart is found to be effective to be applied for temperature monitoring than the existing control chart.
“…Larger ones lead to stronger noise suppression but also longer time periods until an introduced bias becomes visible. Therefore, a trade-off must be made, i.e., maximizing the time between two false positive alerts (“Average Run Length”) on one hand side and minimizing the time to a true positive alert (“Average Delay Time”) for an underlying bias on the other side 22 .…”
Cardiac magnetic resonance (CMR) examinations require standardization to achieve reproducible results. Therefore, quality control as known as in other industries such as in-vitro diagnostics, could be of essential value. One such method is the statistical detection of long-time drifts of clinically relevant measurements. Starting in 2010, reports from all CMR examinations of a high-volume center were stored in a hospital information system. Quantitative parameters of the left ventricle were analyzed over time with moving averages of different window sizes. Influencing factors on the acquisition and on the downstream analysis were captured. 26,902 patient examinations were exported from the clinical information system. The moving median was compared to predefined tolerance ranges, which revealed an overall of 50 potential quality relevant changes (“alerts”) in SV, EDV and LVM. Potential causes such as change of staff, scanner relocation and software changes were found not to be causal of the alerts. No other influencing factors were identified retrospectively. Statistical quality assurance systems based on moving average control charts may provide an important step towards reliability of quantitative CMR. A prospective evaluation is needed for the effective root cause analysis of quality relevant alerts.
“…A diminutive concentration has been brought in to the study control charts based on MA under different lines of work. Some works on MA control chats for various lines of work can be seen in [1][2][3][4][5][6][7].…”
Present article proposes the neutrosophic moving average (NMA) control chart under neutrosophic statistics (NS) based on multiple dependent state (MDS), Repetitive and multiple dependent state repetitive (MDSR) sampling schemes. The neutrosophic moving average control chart is useful to monitor the process mean in the industries when the measurements expressed in terms of uncertainty or fuzzy or interval. In this circumstance, the existing monitoring designs could not be useful for the monitoring of mean accident or injury data. In the present investigation neutrosophic moving average control chart is developed under the NS. The chart coefficients of the proposed control chart are obtained using Monte Carlo simulation under NS. A comparative study between the three sampling schemes of neutrosophic moving average control chart under neutrosophic statistics (NS) is given. Two real examples from accident and injury data are taken to investigate the accomplishment of the proposed chart. Based on the simulation study and real data, the proposed chart is out performed over the existing control charts.
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