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 multivariate statistical process monitoring (MSPM) of the wood kiln drying. A multivariate statistical process monitoring (MSPM) was developed using the SIMCA-P commercial software and applied offline on batches which went out of control (also known as outliers). The method was proven very powerful to detect the abnormalities and then diagnosis the faults. A database of faults and diagnosis is under development and an expert system will be developed for on-line fault detection and diagnosis. The purpose of this paper is not to discuss the theory behind of multivariate data analysis but rather to demonstrate the applicability and the usefulness of the technique.