Control limits

Abstract: In this study, we assessed the usefulness of control charts in combination with the process capability indices, Cpm and Cpk, in the control strategy of an analytical method. The traditional X-chart and moving range chart were used to monitor the analytical method over a 2-year period. The results confirmed that the analytical method is in-control and stable. Different criteria were used to establish the specifications limits (i.e., analyst requirements) for fixed method performance (i.e., method requirements). If the specification limits and control limits are equal in breadth, the method can be considered “capable” (Cpm = 1), but it does not satisfy the minimum method capability requirements proposed by Pearn and Shu (2003). Similar results were obtained using the Cpk index. The method capability was also assessed as a function of method performance for fixed analyst requirements. The results indicate that the method does not meet the requirements of the analytical target approach. A real-example data of a SEC with light-scattering detection method was used as a model whereas previously published data were used to illustrate the applicability of the proposed approach. This article is protected by copyright. All rights reserved

Abstract: A control chart for non-normal correlated data under repetitive sampling is presented in this manuscript. A Burr distribution is employed to model the non-normal distribution. The proposed control chart is based on two sets of control limits whose coefficients are determined by considering the in-control average run length. The tables of the out-of-control average lengths for various shifts, sample size, and correlations are presented in the paper. The proposed control chart is found to be more efficient than the existing control chart based on single sampling in detection of small process shifts.

Abstract: For buildings designed to meet aggressive energy goals, there is a need for tools to assist in the monitoring and maintenance of performance once the building is in operation. In particular, dashboard visualizations that show real-time and historic end use energy consumption alongside expected performance are powerful tools for both occupant engagement and the identification of operational issues. This article focuses on two related approaches to calculating upper and lower control limits for acceptable ranges of end use, which use a combination of modeled and measured usage data to generate realistic energy-conservative control limits. The first approach centers on the analysis of frequency distributions for end use consumption as functions of a main effect variable, while the second approach uses multivariate quantile regression based on principal components to generate control limits from all available measured variables.

Abstract: This study proposed an approach which simultaneously considers the properties of cost and quality by minimum value of expected cost per hour which is restricted by maximum value of type I error (α U ) and minimum value of power (p L ) to determine three parameters (including sample size, sampling interval between successive samples, and the control limits) when an x bar chart supervises a manufacturing process with an increased hazard rate and the measurements within the sample being correlated. Most control chart economic statistical designs assumed the failure mechanism, which belongs to the Poisson distribution. Furthermore, the subgroup measurements within a sample undergo an independent distribution. However, the above-mentioned assumptions are not usually pragmatic. As a result the assumption of Poisson failure mechanism for processes in which machine wear occurs over time is not appropriate. Also, this supposition of independently distributed in the subgroup measurements in which collected from the production process, multiple pins on an integrated circuit chip, may not be tenable. Hence, this study employs combining Rahim and Banerjee's cost model with Yang and Hancock's multivariate normal distribution model to search the optimal parameters of control charts under correlated data. Meanwhile, a genetic algorithm is adopted.