A framework for case-based diagnosis of batch processes in the principal components space

Authors:
Xavier Berjaga;Álvaro Pallarés;Joaquim Meléndez
Affiliations:
Plastiasite S.A., Barcelona, Spain and Institut d'Informàtica i Aplicacions, Universitat de Girona, Girona, Spain;Plastiasite S.A., Barcelona, Spain;Institut d'Informàtica i Aplicacions, Universitat de Girona, Girona, Spain
Venue:
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
Year:
2009

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Abstract

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 pre-process of the data is carried.