Fault Diagnosis of an Industrial Chemical Process using Machine Learning Algorithms: Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA)

Abstract

Abstract Fault diagnosis plays a very important role in today’s complex industrial chemical processes. Intelligent fault diagnosis (IFD) is the term for the application of machine learning ideas to the diagnosis of process faults. These past two or three decades have seen a lot of interest in this promising method for releasing the contribution from human work and automatically recognizing the health statuses of any processes. Detecting the fault and the associated variable for the cause of the fault has high significance as it reduces the waste of resources and ensures production safety. The goal of this research was fault diagnosis of the Tennessee Eastman Process (TEP) using two different machine learning algorithms Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA). PCA and KPCA have been applied with the integration of the Support Vector Machine (SVM) to the data collected to produce a classifier for the different faults in the chemical process. Afterward, the classification results of the two methods have been compared.