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Modelling and Simulation in Engineering
Volume 2014, Article ID 426402, 14 pages
http://dx.doi.org/10.1155/2014/426402
Research Article

Fault Diagnosis of Batch Reactor Using Machine Learning Methods

1Department of Electronics and Instrumentation Engineering, Adhiyamaan College of Engineering, Hosur, Krishnagiri, Tamil Nadu 635 109, India
2Department of Information Technology, Adhiyamaan College of Engineering, Hosur, Krishnagiri, Tamil Nadu 635 109, India
3Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, Tamil Nadu 600 044, India

Received 3 January 2014; Accepted 4 March 2014; Published 22 April 2014

Academic Editor: Azah Mohamed

Copyright © 2014 Sujatha Subramanian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM) is used to estimate the heat release ( ) of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faulty values, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA) techniques. artificial neural network (ANN) classifiers like multilayer perceptron (MLP), radial basis function (RBF), and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter ( ) shows that it is more efficient and fast for diagnosing the typical faults.