期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：3
页码：85-90
出版社：International Journal of Soft Computing & Engineering
摘要：This paper presents a new approach for classification of faults in a process control system with complex overlapping fault classes. It is based on the application of Self Organising Maps that possess the capability of efficient unsupervised learning. Using the SOM training process, the proposed approach derives a set of neurons by considering process monitoring dataset comprising of multiple measured attributes. This set of neurons constitutes the multilayered SOM, in which each neuron corresponds to a class of faults. The neurons with similar attribute values are spatially arranged in adjoining localities, to set up an exploratory linkage between the SOM and the fault dataset. The performance of the proposed method is found to be satisfactory for fault diagnosis in the DAMADICS Benchmark Process Control System, even for the overlapping fault classes that pose considerable difficulty to other classification approaches applied by researchers.
关键词：Artificial Neural Networks; DAMADICS;Benchmark Process Control System; Fault Diagnosis; Self;Organizing Maps.
