Volume 3, Issue 1, May 2013, Pages 138–144
K. Prabakaran1, T. Uma Mageshwari2, D. Prakash3, and A. Suguna4
1 Assistant Professor, Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, 638057, India
2 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
3 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
4 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
Original language: English
Copyright © 2013 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.
As modern process industries become more complex, the importance to detect and identify the faulty operation of pneumatic process control valves is increasing rapidly. The prior detection of faults leads to avoiding the system shutdown, breakdown, raw material damage and etc. The proposed approach for fault diagnosis comprises of two processes such as fault detection and fault isolation. In fault diagnosis, the difference between the system outputs and model outputs called as residuals are used to detect and isolate the faults. But in the control valve it is not an easy process due to inherent nonlinearity. The particular values of five measurable quantities from the valve are depend on the commonly occurring faults such as Incorrect supply pressure, Diaphragm leakage and Actuator vent blockage. The correlations between these parameters from the fault values for each operating condition are learned by a multilayer BP Neural Network. The parameter consideration is done through the committee of Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The simulation results using MATLab prove that BP neural network has the ability to detect and identify various magnitudes of the faults and can isolate multiple faults. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training.
Author Keywords: AI, Atrial fibrillation, Bradycardia, Heart, MATLab, PCG, Tachycardia.
K. Prabakaran1, T. Uma Mageshwari2, D. Prakash3, and A. Suguna4
1 Assistant Professor, Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Thudupathi, Erode, Tamilnadu, 638057, India
2 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
3 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
4 Electrical and Electronics Engineering, Anna University Regional Centre Coimbatore, Coimbatore - 641 047, Tamilnadu, India
Original language: English
Copyright © 2013 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
As modern process industries become more complex, the importance to detect and identify the faulty operation of pneumatic process control valves is increasing rapidly. The prior detection of faults leads to avoiding the system shutdown, breakdown, raw material damage and etc. The proposed approach for fault diagnosis comprises of two processes such as fault detection and fault isolation. In fault diagnosis, the difference between the system outputs and model outputs called as residuals are used to detect and isolate the faults. But in the control valve it is not an easy process due to inherent nonlinearity. The particular values of five measurable quantities from the valve are depend on the commonly occurring faults such as Incorrect supply pressure, Diaphragm leakage and Actuator vent blockage. The correlations between these parameters from the fault values for each operating condition are learned by a multilayer BP Neural Network. The parameter consideration is done through the committee of Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The simulation results using MATLab prove that BP neural network has the ability to detect and identify various magnitudes of the faults and can isolate multiple faults. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training.
Author Keywords: AI, Atrial fibrillation, Bradycardia, Heart, MATLab, PCG, Tachycardia.
How to Cite this Article
K. Prabakaran, T. Uma Mageshwari, D. Prakash, and A. Suguna, “Fault Diagnosis in Process Control Valve Using Artificial Neural Network,” International Journal of Innovation and Applied Studies, vol. 3, no. 1, pp. 138–144, May 2013.
