HE PRINCIPLE of the quality control chart originated with the pioneering work of Shewhart in 1931 (1) in the application of statistical methods to industrial manufacturing problems. Great impetus to the use of these methods occurred during World War II in the fields of chemical process manufacturing and munitions (2). Application of the control chart to the control of accuracy and precision of ...

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a ne...

Analysis of Means (ANOM) is a rational extension of the Shewhart control chart for comparing products from different design configurations in off-line quality improvement. ANOM is a powerful graphic-tool for engineers to present and interpret experimental results. We apply the ANOM idea to the analysis of lifetime data resulting from experiments planned for enhancing product reliability. The ma...

This paper proposes an economic model for the synthetic chart. The synthetic chart is an integration of the X chart and the CRL chart. A simplified algorithm to obtain the optimal parameters of the synthetic chart which minimizes the expected cost function is introduced. Numerical examples based on different values of input parameters are given, and sensitivity analyses of the parameters are pe...

Structural health monitoring is described in the context of a statistical process control paradigm. This paper demonstrates the application of various statistical process control techniques such as the Shewhart, the exponentially weighted moving average, and the cumulative sum control charts to vibration-based damage diagnosis. The control limits are first constructed based on the measurements ...

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 ...