شماره ركورد كنفرانس :
4686
عنوان مقاله :
High dimensional process monitoring using Principle Component Analysis and T2 chart
عنوان به زبان ديگر :
High dimensional process monitoring using Principle Component Analysis and T2 chart
پديدآورندگان :
Jalilibal Zahra zjalili222@gmail.com Shahed University Tehran , Mousavi Seyed Meysam Shahed University Tehran , Amiri Amirhossein Shahed University Tehran
كليدواژه :
Principal component analysis , High dimensional process monitoring , dimension reduction , Hotelling T2
عنوان كنفرانس :
پنجمين كنفرانس بين المللي مهندسي صنايع و سيستم ها
چكيده فارسي :
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
چكيده لاتين :
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
