Control charts are the most popular monitoring tools used to distinguish between special (assignable) and common causes of variation and to detect any changes in processes. The time that a control chart gives an out of control signal is not the real time of change. The actual time of the change is called the change point. Knowing the real time of the change will help and simplify finding the as...

Cycle-based signals are generally obtained through the automatic sensing of critical process variables during each repetitive operation cycle of a manufacturing process, and they thus contain a significant amount of information about the process condition. Increasing attention has been paid recently to the problem of effectively monitoring these signals as an aid to the detection of process cha...

Not all data in practice came from a process with normal distribution. When the process distribution is non-normal or unknown, the commonly used Shewhart control charts are not suitable. In this paper, a new non-parametric CUSUM Mean Chart is proposed to monitor the possible small mean shifts in the process. The sampling properties of the new monitoring statistics are examined and the average r...

The most commonly used statistical process control charts to detect special causes are Shewhart and Cusum charts. However, the Cuscore chart is a contemporary alternative that is especially well suited for detecting special causes that can be modeled in advance based on their characteristic effect on the system. In this paper, we develop the appropriate Cuscore statistic and the required contro...

To keep an eye on the status of high-quality processes for fraction nonconforming, synthetic double sampling (SDS) np chart is a helpful tool. The SDS hybrid between (DS) and conforming run length (CRL) chart. performance control typically judged solely using average (ARL). However, as shape (RL) distribution varies with magnitude shift in process ARL no longer provides clear interpretation cha...

This paper proposes two alternative schemes for the online recognition of control chart patterns (CCPs), namely: 1 a scheme based on direct continuous recognition 2 a scheme based on ‘recognition only when necessary’. The study focuses on recognition of six CCPs plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trend-down and cyclic. The artificial neural netw...

Use of risk adjusted control charts for monitoring patients’ surgical outcomes is now popular.These charts are developed based on considering the patient’s pre-operation risks. Change point detection is a crucial problem in statistical process control (SPC).It helpsthe managers toanalyzeroot causes of out-of-control conditions more effectively. Since the control chart signals do not necessarily...

when a change occurs in a process, one expects to receive a signal from a control chart as quickly as possible. upon the receipt of signal from the control chart a search for identifying the source of disturbance begins. however, searching for assignable cause around the signal time, due to the fact that the disturbance may have manifested itself into the rocess sometimes back, may not always l...

This paper presents the economic design of modified X chart for autocorrelated data and comparison with the economic design of Shewhart’s X chart. An attempt has been made to counter autocorrelation by designing the modified X chart; as the cost of operating a process control system is an important element in the economic design of control charts. The economic designs of both modified and Shewh...

Purpose The purpose of this paper is to evaluate the performance a modified EWMA control chart ( γ chart), which considers data distribution and incorporate its correlation structure, simulating in-control out-of-control processes select an adequate value for smoothing parameter with these conditions. Design/methodology/approach This based on simulation approach using methodology evaluating sta...