Control charts are the most popular Statistical Process Control (SPC) tools used to monitor process changes. When a control chart indicates an out of control signal it means that the process has changed. However control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifyi...

One of the hallmarks of statistical thinking is the importance of measuring and understanding variability. The Shewhart Control Chart, which separates special cause from common cause variation, is one of the most important tools for understanding the current state of a process. The analysis of variance (ANOVA) is another statistical tool for splitting variability into component sources. These c...

Statistical process control (SPC) is a method of monitoring, controlling, and improving a process through statistical analysis. An important SPC tool is the control chart, which can be used to detect changes in production processes, including animal production systems, with a statistical level of confidence. This paper introduces the philosophy and types of control charts, design and performanc...

The control chart is a very popular tool of statistical process control. It is used to determine the existence of special cause variation to remove it so that the process may be brought in statistical control. Shewhart-type control charts are sensitive for large disturbances in the process, whereas cumulative sum (CUSUM)–type and exponentially weighted moving average (EWMA)–type control charts ...

Unlike a Shewhart chart, the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts are memory control charts (also known as time weighted control charts) that are used for a quick detection of small shifts in the process mean. Control charts that combine information from present and past samples, like the EWMA and CUSUM charts have the ability to detect process changes ...

Some of the most widely-investigated control charting techniques for autocorrelated data are based on time series residuals. If the mean shift in the autocorrelated process is a sudden step shift, the resulting mean shift in the residuals is time varying and has been referred to as the fault signature. Traditional residual based charts, such as a Shewhart, CUSUM, or EWMA on the residuals, do no...

SubmitJames 0. Westgard, Patricia L. Barry, and ters: Marian R. Hunt, Depts. of Medicine, Pathology, and Clinical Laboratories, University of Wisconsin, Center for Health Sciences, Madison, WI 53792 Torgny Groth, Group for Biomedical Informatics, Uppsala University Data Center, S-75002 Uppsala, Sweden ReviewRobert W. Burnett, Clinical Chemistry ers: Laboratory, Hartford Hospital, Hartford, CT 0...

Abstract Machine learning-based condition monitoring of wind turbines’ critical components is an active area research, especially for pitch systems, which suffer from a high failure rate. In this work, we successfully predicted and detected the high-temperature fault electric motor by analyzing SCADA data through ensemble approach. For that, normal behavior models to predict temperature were co...

Quality control charts indicate out of control conditions if any nonrandom pattern of the points is observed or any point is plotted beyond the control limits. Nonrandom patterns of Shewhart control charts are tested with sensitizing rules. When the processes are defined with fuzzy set theory, traditional sensitizing rules are insufficient for defining all out of control conditions. This is due...