The goal of Phase I monitoring of multivariate data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. However, they are dif...

At present, due to the rapid development of domestic economy, environmental pollution problems are becoming increasingly serious, especially such as PM2.5 and PM10 caused by industrial emissions waste gas most urgent. The general linear profile model multivariate exponential weighted moving average control chart (MEWMA) is designed perform monitoring evaluating air quality index (AQI) in Tianji...

Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise input representation techniques, and through modular and integrated recognizer designs. There is al...

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been ...