Data Driven Approach to Monitoring and Fault Detection in Process Control Plants

Varghese, Linda and George, V I and Makkithaya, Krishnamoorthi and Kumar, Abhishekh (2015) Data Driven Approach to Monitoring and Fault Detection in Process Control Plants. International Journal of Control Theory and Applications, 8 (3). pp. 1121-1128. ISSN 09745572

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Detecting faults and monitoring is a very important activity in process control plants for increasing the efficiency of the plant. Data driven approach is particularly helpful for fault detection, if the underlying mathematical model of the process is very complex. It can be used to create automatic systems which accurately predict whether the operating condition of the plant is normal or faulty. This paper compares different supervised learning algorithms, in order to detect fault in process control plant. The algorithms are tested in Matlab environment. Finally, all the models give satisfactory accuracy while detecting two different types of faults as well as normal operating condition

Item Type: Article
Uncontrolled Keywords: Fault detection, learning from examples, data mining, prediction, Matlab, supervised learning
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Instrumentation and Control
Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 13 Feb 2016 13:10
Last Modified: 13 Feb 2016 13:10

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