Fault Diagnosis of Batch Reactor Using Machine Learning Methods
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 estima...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/426402 |
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| author | Sujatha Subramanian Fathima Ghouse Pappa Natarajan |
| author_facet | Sujatha Subramanian Fathima Ghouse Pappa Natarajan |
| author_sort | Sujatha Subramanian |
| collection | DOAJ |
| description | 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 (Qr) 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 Qr 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 (Qr) shows that it is more efficient and fast for diagnosing the typical faults. |
| format | Article |
| id | doaj-art-3ac3e9814c5942b094384ab233da66a2 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-3ac3e9814c5942b094384ab233da66a22025-02-03T06:01:39ZengWileyModelling and Simulation in Engineering1687-55911687-56052014-01-01201410.1155/2014/426402426402Fault Diagnosis of Batch Reactor Using Machine Learning MethodsSujatha Subramanian0Fathima Ghouse1Pappa Natarajan2Department of Electronics and Instrumentation Engineering, Adhiyamaan College of Engineering, Hosur, Krishnagiri, Tamil Nadu 635 109, IndiaDepartment of Information Technology, Adhiyamaan College of Engineering, Hosur, Krishnagiri, Tamil Nadu 635 109, IndiaDepartment of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, Tamil Nadu 600 044, IndiaFault 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 (Qr) 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 Qr 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 (Qr) shows that it is more efficient and fast for diagnosing the typical faults.http://dx.doi.org/10.1155/2014/426402 |
| spellingShingle | Sujatha Subramanian Fathima Ghouse Pappa Natarajan Fault Diagnosis of Batch Reactor Using Machine Learning Methods Modelling and Simulation in Engineering |
| title | Fault Diagnosis of Batch Reactor Using Machine Learning Methods |
| title_full | Fault Diagnosis of Batch Reactor Using Machine Learning Methods |
| title_fullStr | Fault Diagnosis of Batch Reactor Using Machine Learning Methods |
| title_full_unstemmed | Fault Diagnosis of Batch Reactor Using Machine Learning Methods |
| title_short | Fault Diagnosis of Batch Reactor Using Machine Learning Methods |
| title_sort | fault diagnosis of batch reactor using machine learning methods |
| url | http://dx.doi.org/10.1155/2014/426402 |
| work_keys_str_mv | AT sujathasubramanian faultdiagnosisofbatchreactorusingmachinelearningmethods AT fathimaghouse faultdiagnosisofbatchreactorusingmachinelearningmethods AT pappanatarajan faultdiagnosisofbatchreactorusingmachinelearningmethods |