High dimensional process monitoring using Principle Component Analysis and T2 chart

High dimensional process monitoring using Principle Component Analysis and T2 chart

Statistical process monitoring is an essential need for industrial processes. Many of these processes apply principal component analysis to perform statistical process monitoring as its simplicity in computations. The PCA is used in this study to reduce dimension for monitoring high dimensional process which has complex computations. Fault detection charts that are commonly employed with the PCA method are the Hotelling T2 statistic which are used for monitoring the process which is reduced by PCA. This study has two steps; first, the high dimensional process is reduced by applying PCA, and then, the reduced process is monitored

Statistical process monitoring is an essential need for industrial processes. Many of these processes apply principal component analysis to perform statistical process monitoring as its simplicity in computations. The PCA is used in this study to reduce dimension for monitoring high dimensional process which has complex computations. Fault detection charts that are commonly employed with the PCA method are the Hotelling T2 statistic which are used for monitoring the process which is reduced by PCA. This study has two steps; first, the high dimensional process is reduced by applying PCA, and then, the reduced process is monitored