Online batch fault diagnosis with Least Squares Support Vector Machines*

A new fault identification method for batch processes based on Least Squares Support Vector Machines (LS-SVMs; Suykens et al. [2002]) is proposed. Fault detection and fault diagnosis of batch processes is a difficult issue due to their dynamic nature. Principal Component Analysis (PCA)-based techniques have become popular for data-driven fault detection. While improvements have been made in handling dynamics and non-linearities, correct fault diagnosis of the process disturbance remains a difficult issue. In this work, a new data-driven diagnosis technique is developed using an LS-SVMs based statistical classifier. When a fault is detected, a small window of pretreated data is sent to the classifier to identify the fault. The proposed approach is validated on data generated with an expanded version of the Pensim simulator [Birol et al., 2002]. The simulated data contains faults from six different classes. The obtained results provide a proof of concept of the proposed technique and demonstrate the importance of appropriate data pretreatment.