A framework for Case-Based Diagnosis of batch processes in the principal components space

This paper presents a framework for fault detection and diagnosis of batch processes based on the information directly gathered from sensors. First, a statistical model of the process is build using multiway principal component analysis (MPCA) for dimensionality reduction and fault detection tasks. Afterwards, a case-based reasoning (CBR) approach is used for fault diagnosis and for false alarm and missed detection reduction. This framework has been tested in two completely different fields: power quality monitoring for relative location of voltage sags and injection moulding processes for faulty sensor detection and diagnosis. Results obtained show that this framework presents a good performance and is general enough to be applied to any field, if the appropriate preprocess of the data is carried.