Classifying pilot-plant distillation column faults using neural networks

Abstract

An approach to fault detection is described which uses neural-network pattern classifiers trained using data from a rigorous differential-equation-based simulation of a pilot-plant column. Two cases studies are presented, both considering only plant data. For two classes of process data, a neural network and a K-Means classifier both produced excellent diagnoses. Extending the study to include three additional classes of plant operation, a neural network again gave accurate classifications, while a K-Means classifier failed to correctly categorise the data. Principal components analysis is used to visualise data clusters. The robustness of the neural networks was found to be generally good.