A Distribution-free Control Chart For The Joint Monitoring Of Location And Scale

In the traditional quality control process,the mean and variance of the attribute characteristics are often monitored separately,and most of the control charts need to assume that the controlled distribution is normal.But this assumption is unrealistic in many situation,where insufficient data are available to allow this distribution.So seldom know in-control chart distribution.However,distribution-free control chart that not assume any specific form for the process distribution.In this paper,we propose a new exponentially weighted moving average control chart based on the Cram?er-von Mises(CvM)test for joint monitoring of location and scale,called ECvM chart.Results show that compare with other distribution-free charts,in most cases our new chart has satisfied performance.The control limits based on Monte-Carlo simulation are provided in a table.The in-control and out-of-control performance properties of the chart are investigated in simulation studies in terms of the means,the standard deviation,and some percentiles of the length distribution,average run length and standard deviation of run length.The results show satisfactory performance in detecting various process shift.The application of our proposed chart is illustrated by a real example.