Volume 8, Issue 3, September 2014, Pages 1361–1369
K. Prabakaran1, S. Kaushik2, R. Mouleeshuwarapprabu3, and Ajith B. Singh4
1 Assistant Professor, Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, 638057, India
2 Assistant Professor, Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathy, Erode, Tamilnadu, 638401, India
3 Assistant Professor, Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, 638052, India
4 Assistant Professor, Electronics and Instrumentation Engineering, Sethu institute of technology, Kariapatti, Tamilnadu, 626115, India
Original language: English
Copyright © 2014 ISSR Journals. 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.
Fault diagnosis is an ongoing significant research field due to the constantly increasing need for maintainability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A Self-Organizing Map based approach is implemented to detect the external faults such as Actuator vent blockage, Diaphragm leakage and in correct supply pressure. The Self-Organizing Map is able to identify the actuator condition with high accuracy by monitoring five parameters. The parameter selection is based on the committee of DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). The Self-Organizing Map Systems were implemented in real time using MATLAB and the results prove that the system can effectively classify all the types of external faults.
Author Keywords: Actuators, Fault Diagnosis, Fault Isolation, External Fault, Neural Network.
K. Prabakaran1, S. Kaushik2, R. Mouleeshuwarapprabu3, and Ajith B. Singh4
1 Assistant Professor, Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, 638057, India
2 Assistant Professor, Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathy, Erode, Tamilnadu, 638401, India
3 Assistant Professor, Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode, Tamilnadu, 638052, India
4 Assistant Professor, Electronics and Instrumentation Engineering, Sethu institute of technology, Kariapatti, Tamilnadu, 626115, India
Original language: English
Copyright © 2014 ISSR Journals. 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 is an ongoing significant research field due to the constantly increasing need for maintainability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A Self-Organizing Map based approach is implemented to detect the external faults such as Actuator vent blockage, Diaphragm leakage and in correct supply pressure. The Self-Organizing Map is able to identify the actuator condition with high accuracy by monitoring five parameters. The parameter selection is based on the committee of DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). The Self-Organizing Map Systems were implemented in real time using MATLAB and the results prove that the system can effectively classify all the types of external faults.
Author Keywords: Actuators, Fault Diagnosis, Fault Isolation, External Fault, Neural Network.
How to Cite this Article
K. Prabakaran, S. Kaushik, R. Mouleeshuwarapprabu, and Ajith B. Singh, “Self-Organizing Map Based Fault Detection and Isolation Scheme for Pneumatic Actuator,” International Journal of Innovation and Applied Studies, vol. 8, no. 3, pp. 1361–1369, September 2014.
