Use of Multivariate Data Analysis for Lumber Drying Process Monitoring and Fault Detection

Process monitoring refers to the task of detecting abnormal process operations resulting from the shift in the mean and/or the variance of one or more process variables. To successfully operate any process it is important to detect and diagnose any process upsets, equipment failures or other events that may have significant impact on energy consumption and productivity. In most manufacturing processes, it is difficult if not impossible to detect abnormal operation by simply tracking some physical variable such as temperatures and pressures. Lumber drying (batch process) performance depends on more than 200 variables making the process very difficult to model and control using classical methods. Multivariate data analysis (MVDA) as a data mining technique makes the task easier and allows early fault detection thus allowing acting well before process goes out of control. MVDA techniques (PCA, PLS) were successfully applied on historical data of a sawmill operation to develop a multivaria...

• Type: Conference
• Year: 2006
• Where: DMIN
• Authors: Mouloud Amazouz, Radu Pantea
• Keywords: Abnormal Process Operations, DMIN 2006, DMIN 2007, Multivariate Data Analysis, Multivariate Statistical Process