Abstract
AbstractOwing to the limited number of inspections during a short run process, it is impossible to get the correct estimate of the population mean and standard deviation during Phase I implementation of control chart. Thetcontrol chart proposed recently can overcome this problem. The EWMAtcontrol chart has been proposed to monitor the process mean, but a single EWMAtcontrol chart cannot perform well for small and large shifts simultaneously, which is known as the “inertia problem”. The adaptive varying smoothing parameter EWMA (AEWMA) control chart can overcome the inertia problem. In this paper, the AEWMAtcontrol chart for short run process is proposed. The truncated average run length and the probability of trigger a signal are adopted to test the performance of short run AEWMAtchart. Based on the investigation of the joint effect of control chart parameters on the performance of AEWMAtchart, a new optimization algorithm is proposed for statistical design of the AEWMA control chart. Simulations are performed for perfect and imperfect setup conditions, the results show that the AEWMAtcontrol chart performs better than the EWMAtcontrol chart.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
