Distribution-free composite Shewhart-GWMA Mann-Whitney charts for monitoring the process location

The Mann-Whitney (MW) statistic is one of the most recommended two-sample statistical tests when the assumption of normality fails to hold due to its robustness and fascinating properties especially when small sample sizes are involved. In order to improve the sensitivity of the generally weighted moving average (GWMA) monitoring scheme toward the detection of large shifts, in this paper, a new distribution-free phase II composite Shewhart-GWMA (CSG) scheme is proposed using the MW U statistic. The performance of the proposed scheme is investigated using the average run-length (ARL) and average extra quadratic loss (AEQL) values through extensive simulations. The performance of this newly proposed monitoring scheme is found to be superior when compared to numerous memory-type MW schemes (some existing and others introduced in this paper) in many situations. An illustrative example is given to demonstrate the implementation of the CSG MW U scheme using real-life data.     

A hybrid homogeneously weighted moving average control chart for process monitoring: Discussion

The sensitivity of a monitoring scheme depends on many factors including the variance of the charting statistic which is very important in the computation of the control limits. This paper discusses the computation of the variance of the recently proposed hybrid homogeneously weighted moving average (HHWMA) $\overline{X}$ scheme which was based on an incorrect assumption. The correct variance is used to evaluate the run-length characteristics of the HHWMA $\overline{X}$ scheme. It is observed that the incorrect variance has a significant impact on the sensitivity (or performance) of the HHWMA $\overline{X}$ scheme.     

On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme.     

Generally weighted moving average monitoring schemes: Overview and perspectives

An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution-free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (ie, double GWMA, composite Shewhart-GWMA, mixed GWMA-CUSUM, and mixed CUSUM-GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA-related schemes (ie, 66 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.     

Reconnaissance et traitement de cavites naturelles ou artificielles dans le domaine ferroviaire

Bulletin of Engineering Geology and the Environment - L'existence de cavités souterraines et de réseaux karstiques peut poser des problèmes de stabilité des plate-formes,...     

Effect of watershed parameters on mercury distribution in different environmental compartments in the Mobile Alabama River Basin, USA

Total mercury (THg) and mono-methylmercury (MeHg) levels in water, sediment, and largemouth bass (LMB) (Micropterus salmoides) were investigated at 52 sites draining contrasting land use/land cover and habitat types within the Mobile Alabama River Basin (MARB). Aqueous THg was positively associated with iron-rich suspended particles and highest in catchments impacted by agriculture. Sediment THg was positively associated with sediment organic mater and iron content, with the highest levels observed in smaller catchments influenced by wetlands, followed by those impacted by agriculture or mixed forest, agriculture, and wetlands. The lowest sediment THg levels were observed in main river channels, except for reaches impacted by coal mining. Sediment MeHg levels were a positive function of sediment THg and organic matter and aqueous nutrient levels. The highest levels occurred in agricultural catchments and those impacted by elevated sulfate levels associated with coal mining. Aqueous MeHg concentrations in main river channels were as high as those in smaller catchments impacted by agriculture or wetlands, suggesting these areas were sources to rivers. Elevated Hg levels in some LMB were observed across all types of land use and land cover, but factors such as shallow water depth, larger wetland catchment surface area, low aqueous potassium levels, and higher Chl a concentrations were associated with higher Hg burdens, particularly in the Coastal Plain province. It is suggested that the observed large variability in LMB Hg burdens is linked to fish displacement by anglers, differences in food web structure, and sediment biogeochemistry, with surficial sediment iron oxides buffering the flux of MeHg from sediments to deeper water pelagic food webs.