Title: Self-organised maps for online detection of faults in non-linear industrial processes
Authors: M. Jeevan, S. Babji, A.K. Tangirala
Addresses: Department of Chemical Engineering, Indian Institute of Technology, Chennai 600036, Madras, Tamilnadu, India. ' Department of Chemical Engineering, Indian Institute of Technology, Chennai 600036, Madras, Tamilnadu, India. ' Department of Chemical Engineering, Indian Institute of Technology, Chennai 600036, Madras, Tamilnadu, India
Abstract: Fault detection in linear systems is a fairly matured area where the well-known principal component analysis (PCA) and its variants are widely used. However, a large class of non-linear systems exist, especially chemical processes, on which such techniques cannot be applied. The present work aims at demonstrating the application of self-organising maps (SOM) for fault detection in non-linear processes. SOM belongs to the class of unsupervised and competitive learning algorithms and it is highly capable of handling nonlinear relationships. Application of SOM to fault detection involves generation of a reference template for the process under fault-free conditions. Online fault detection is performed by generating a new template using a windowing of the data, which is compared with the reference template using a novel metric based on the node weights obtained from SOM to detect possible faults in the process. Simulation studies on two non-linear systems, namely, (1) continuously stirred tank reactor (CSTR) and (2) bioreactor process demonstrate the practicality and utility of the proposed method.
Keywords: fault detection; nonlinear systems; SOM; self-organising maps; PCA; principal component analysis; simulation; continuously stirred tank reactors; CSTR; bioreactors.
International Journal of Automation and Control, 2010 Vol.4 No.3, pp.271 - 283
Published online: 02 Jun 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article