Error Detection and Pattern Prediction Through Phase II Process Monitoring

Abstract

The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended exponentially weighted moving average control chart based on the percentiles estimator performs better than exponentially weighted moving average control charts based on the percentiles estimator and modified maximum likelihood estimator. Further, these results will help the firms in the early detection of errors that enhance the process reliability of the telecommunications and financing industry.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

“…The proposed scheme of DMR employs only two redundant filters instead of three in the existing scheme and as such reduces the area on the chip [19]. It can correct errors in the outputs of the filters only in any one of the outputs y1, y2, y3, and y4 at a time.…”

Control theory for skewed distribution under operation side of the telecommunication industry and hard-bake process in the semiconductor manufacturing process

“…The percentile estimator (PE) for the shape parameter of KL2PFD is also discussed in Zaka 34 is given by…”

Classical estimator based modified control charts for phase‐II monitoring in real life

“…In literature, we see some control charts constructed based on assumption of normality such as by Page, 1 Roberts, 2 Shamma and Shamma, 3 Haq, 4 Abbas et al, 5 Zwetsloot et al, 6 Sanusi et al, 7 Ajadi and Riaz, 8 Haq and Munir, 9 Li et al, 10 Nguyen et al, 11 Hossain et al, 12 Abu-Shawiesh and Saeed. 13 Some literature on control charts when the assumption of normality is not fulfilled such as Noorossana et al, 14 Lin et al, 15 Hossain et al, 16 Erto et al, 17 Karagoz, 18 Liang et al, 19 Ahmed et al, 20 Khan et al, 21 Saeed and Abu-Shawiesh, 22 and Zaka et al [23][24][25] proposed the control charts for the shape parameter of reflected power, power and weighted power function distributions under classical estimators, Jabeen and Zaka 26 discussed the modified EWMA control charts for the shape parameter of weighted power function distribution, Zaka et al 27 proposed the attribute control charts of the modified power function distribution and Zaka et al 28 discussed the modified EEWMA control charts for monitoring the shape parameter of reflected power function distribution.…”

“…proposed the control charts for the shape parameter of reflected power, power and weighted power function distributions under classical estimators, Jabeen and Zaka 26 discussed the modified EWMA control charts for the shape parameter of weighted power function distribution, Zaka et al 27 . proposed the attribute control charts of the modified power function distribution and Zaka et al 28 . discussed the modified EEWMA control charts for monitoring the shape parameter of reflected power function distribution.…”