Performance evaluation of conventional exponentially weighted moving average (EWMA) and p-value cumulative sum (CUSUM) control chart

This paper is aimed at comparing the performances of the conventional Exponentially Weighted Moving Average (EWMA) and p-value Cumulative Sum (CUSUM) control chart. These charts were applied in monitoring the outbreak of pulmonary tuberculosis in Delta State University Teaching Hospital (DELSUTH), Oghara for a period of eighty four (84) calendar months. Line chart and histogram were plotted to test for stationary and normality of the data. Autocorrelation plot was also used to study the randomness of the data. The results of the control charts show that conventional EWMA chart detects shifts faster in monitoring process mean than the p-value CUSUM control chart. Keywords and Phrases: Exponentially Weighted Moving Average (EWMA), p-value, Cumulative Sum (CUSUM), Autocorrelation, Randomnes

False Discovery Rate-Adjusted Charting Schemes for Multistage Process Monitoring and Fault Identification

Most statistical process control research focuses on single-stage processes. This article considers the problem of multistage process monitoring and fault identification. This problem is formulated as a multiple hypotheses testing problem; however, as the number of stages increases, the detection power of multiple hypotheses testing methods that seek to control the type I error rate decreases dramatically. To maintain the detection power, we use a false discovery rate (FDR) control approach, which is widely used in microarray research. Two multistage process monitoring and fault identification schemes-an FDR-adjusted Shewhart chart and an FDR-adjusted cumulative sum (CUSUM) chart-are established. To apply the FDR approach. the distribution of the CUSUM statistics are obtained based on Markov chain theory and Brownian motion with drift models. The detection and fault identification power of the new schemes are evaluated by the Monte Carlo method. The results indicate that the novel FDR-adjusted approaches are better at identifying the faulty stage than the conventional type I error rate control approach, especially when multiple out-of-control stages are present