Monitoring process variability is essential for establishing efficient process-control schemes. In practice, when the mean levels of a parameter are constant, and the process variance (or standard deviation) is independent of the mean, then, the process variability is typically monitored using conventional Shewhart R or S charts. However, in some practical situations, the mean levels are not constant, and the variance is not independent of the mean. In such cases, the coefficient of variation (CV) is often constant, and thus, CV control charts are generally used to address the issue of the variability in the process. In this paper, new CV charts based on ranked sampling schemes are proposed to enhance the monitoring power of the traditional CV chart. The charts are established based on ranked set sampling (RSS), median RSS (MRSS), and extreme RSS (ERSS), and are examined in terms of their run length performance. The efficiency of the proposed charts is compared with the existing classical CV chart under simple random sampling (SRS) scheme. The results, based on simulation study, indicate that the newly developed ranked-based CV charts show better detection of monitoring signals in process CV than the classical CV chart. In particular, the CV chart based on the ERSS technique performs notably better. A real life example concerning the monitoring of outlet temperature is also provided to illustrate the application of the proposed charts.

In statistical process control applications, profiles functions are considered an efficient way of representing quality of products or processes. Classical and Bayesian thoughts are two chief sources of defining control charting structures for profiles monitoring. This Study introduces novel Bayesian CUSUM control structures for profiles monitoring. The comprehensive comparative study identifies that the proposed Bayesian CUSUM control charts under conjugate priors has better expected performance than competing methods. The implementation of Bayesian structures requires detailed information about process parameters which come up with considerable benefits. In addition, simulative example and case study further justified the superiority of proposed techniques.

A profile is a functional relationship between two or more variables used to monitor the process performance and its quality. Sometimes, the aforementioned relationship is linear or nonlinear depending upon the situation. A monitoring method based on the linear profiles is known as linear profiling which is commonly used due to its simplicity and efficacy. Linear profiling methods have been studied by many researchers with a fixed effect model. However random effect model provides a more suitable interpretation as compared to the fixed effect model under different real-time monitoring methods. Therefore in this article, we are intended to propose a linear profiling EWMA method (EWMAx[R]-3 chart) and MEWMAx[R] chart based on the random effect model using different ranked set sampling techniques such as ranked set sampling (RSS), extreme RSS (ERSS), median RSS (MRSS), double RSS (DRSS), double ERSS (DERSS) and double MRSS (DMRSS). The ranked set sampling (RSS) schemes are not only cost-effective method but also an efficient mechanism as compared to simple random sampling. A designed simulation study used Average Run Length (ARL) as an evaluation measure to witness the detection ability of newly offered EWMAx[R]-3 chart, MEWMAxR chart and existing EWMAx[SRS]-3 chart. The extensive simulation showed that the proposed EWMAx[R]-3 chart and MEWMAxR chart have superiority to detect faults in the process compared to a competitive counterpart. The results are further justified with real data application related to a combined cycle power plant.