Many extensions and modifications have been made to standard process monitoring
methods such as the exponentially weighted moving average (EWMA) chart and the …

Conventional artificial neural networks are inherently equipped with an ambiguous
(uncertain) structure which is hard to be quantified and explained. Time series forecasting …

The exponentially weighted moving average (EWMA) control chart is a memory‐type chart
known to be more efficient in detecting small and moderate shifts in the process parameter …

Control charts for two-parameter exponential distributions are widely used in monitoring
quality and lifetime processes, and more useful and flexible than the chart for one parameter …

Several versions of the exponentially weighted moving average (EWMA) control chart, such
as the generally weighted moving average (GWMA), the double, triple, and quadruple …

The homogeneously weighted moving average (HWMA) control chart is a new memory-type
chart that allocates a specific weight to the current sample and the remaining weight is …

Sudden and sequential variations are crucial in industrial and production processes. To
track these consistent changes in process parameters, effective charting methods are …

We argue against the use of generally weighted moving average (GWMA) control charts.
Our primary reasons are the following:(1) There is no recursive formula for the GWMA …

Classical monitoring schemes are typically designed under the assumption of known
process parameters, perfect measurements and normality. In real-life applications, these …

Monitoring Weibull time between event (TBE) processes is essential to avoid deterioration of
quality characteristics in various reliability analysis problems. Statistical process monitoring …