Monitoring non-parametric profiles using adaptive EWMA control chart

Abstract

To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts.

A novel application of statistical process control charts in financial market surveillance with the idea of profile monitoring

“…There is a fundamental difference here with most of the existing researches in the area of profile monitoring such as Kang and Albin [ 26 ], Li and Wang [ 59 ], Mahmood, Riaz [ 60 ] and Abbasi, Yeganeh [ 61 ] as they have usually considered fixed (constant) explanatory variables in each profile (it means that the j index is omitted from Eq ( 1 ) in their approach). But this assumption is not valid in our problem since the BTC values are not constant in each day.…”

An enhanced nonparametric quality control chart with application related to industrial process

“…We have also assessed the performance of the NPSR-DH chart by using the other measures of the RL such as (standard deviation of the RL (SDRL), a median of the RL (MDRL), and some percentile points of the RL ( )) because of the skewed behavior of the RL distribution (cf. Naveed et al 34 , Bataineh et al 35 and Shafqat et al 36 ). The RL measures are calculated through Monte Carlo simulations in R language.…”

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

“…Although there are two main simultaneous monitoring schemes, one uses only one single chart and the other uses two charts, the former is preferred due to its simplicity of usage. Some notable works that have considered simultaneous monitoring of profiles parameters are Zhang et al 18 , Eyvazian et al 15 , Khedmati & Niaki 19 , Ghashghaei & Amiri 20 , 21 , Mahmood et al 22 , Saeed et al 23 , Ghashghaei et al 24 , Malela-Majika et al 25 , Abbasi et al 26 , Sabahno & Amiri 27 , and Sherwani et al 28 . Ghashghaei & Amiri 20 , developed two memory-type control charts by using a max-operator, namely Max-MEWMA (multivariate EWMA) and Max-MCUSUM (multivariate CUSUM) control charts for simultaneous monitoring of the mean vector and variance-covariance matrix of multivariate multiple linear profiles.…”