There are many practical situations in which the underlying process distribution is either completely unknown or partially known but deviates from normality. In such cases, the performance of the traditional parametric control charts deteriorates, and nonparametric control charts are considered as a robust alternative for monitoring the process. In this article, two new nonparametric homogeneously weighted moving average (NPHWMA) control charts are proposed for monitoring the deviations in process location from the target value. The proposed charts assign a specific weight to the current sample information, and the remaining weight is equally distributed among the previous samples. The proposed NPHWMA charts are based on sign test and Wilcoxon signed-rank test for process monitoring under the skewed and symmetric distributions, respectively. The Monte Carlo simulations are used to study the run length properties and compare the performance of the proposed charts with some existing control charts. It is found that the proposed charts outperform their existing counterparts. Empirical illustrations are provided for practical implementation of the proposed charts.

In this paper, two attribute control charts under multiple dependent state sampling (MDS) and its modification are proposed to monitor the multivariate Poisson count data. The sensitivity of the MDS is increased by introducing an additional parameter. The new method is named as generalized multiple dependent state sampling (GMDS). The proposed charts are based on two pairs of limits i.e. inner and outer control limits that make use of the past samples information in addition to the current sample. The monitoring statistics of the proposed charts are the sum of non-conformities or flaws of each quality characteristics that make the charting structure relatively simple. The performance comparison of the proposed charts with the existing multivariate Poisson control chart is made by using out-of-control average run length (ARL). The comparison reveals that the ARLs of the proposed charts are considerably smaller than the existing scheme for all shift sizes and different values of parameters. Simulation study also showed the dominance of the proposed charts over other existing control charts in detecting the process shift. For practical application, an empirical illustration is provided to signify the performance evaluation of the proposed charts.

This study aims to develop a nonparametric mixed exponentially weighted moving average-moving average (NPEWMA-MA) sign control chart for monitoring shifts in process location, particularly when the distribution of a critical quality characteristic is either unknown or non-normal. In literature, the variance expression of the mixed exponentially weighted moving average-moving average (EWMA-MA) statistic is calculated by allowing sequential moving averages to be independent, and thus the exclusion of covariance terms results in an inaccurate variance expression. Furthermore, the effectiveness of the EWMA-MA control chart deteriorates when the distribution of a critical quality characteristic deviates from normality. The proposed NPEWMA-MA sign control chart addresses these by utilizing the corrected variance of the EWMA-MA statistic and incorporating the nonparametric sign test into the EWMA-MA charting structure. The chart integrates the moving average (MA) statistic into the exponentially weighted moving average (EWMA) statistic. The EWMA-MA charting statistic assigns more weight to recent w samples, with weights for previous observations decling exponentially. Monte Carlo simulations assess the chart’s performance using various run length (RL) characteristics such as average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL). Additional measures for overall performance include the average extra quadratic loss (AEQL) and relative mean index (RMI). The proposed NPEWMA-MA sign control chart demonstrates superior performance compared to existing nonparametric control charts across different symmetrical and asymmetric distributions. It efficiently detects process shifts, as validated through both a simulated study and a real-life example from a combined cycle power plant.

In this paper, a new nonparametric control chart based on two EWMA statistics is proposed for monitoring process deviation from the target value. The performance comparison of the proposed chart with the existing nonparametric EWMA sign control chart and a nonparametric CUSUM mean control chart are made by using out-of-control average run length. The simulation study showed the superiority of the suggested chart over the four other parametric and nonparametric charts. An empirical illustration is also provided for performance evaluation of the proposed chart. By comparing the proposed chart with the existing charts, it is concluded that the proposed chart is more sensitive in detecting a small shift in the process.

Tukey’s control chart is generally used for monitoring the processes where the measurement process physically damages the product. It is based on single observation and robust to outliers. In this paper, two optimal synthetic Tukey’s control charts are proposed by integrating the conforming run length chart with the Tukey’s control chart and its modification. The performance comparison of the proposed charts with the existing Tukey’s control charts is made by using out-of-control average run length and extra quadratic loss as performance metrics. The proposed charts offer better protection against the process shifts as compare to the existing Tukey’s control charts when the underlying process distribution is symmetric or asymmetric. Simulation studies also establish the supremacy of the proposed control charts over the existing Tukey’s control charts. In the end, an illustrative example based on a real data set of the combined cycle power plant is provided for practical implementation.