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Takagi-Sugeno Type Fault Detection and Isolation Scheme for Pneumatic Process Control Valve Using Adaptive Neuro Fuzzy Inference System


Prabakaran K , Erode Sengunthar Engineering College, Thudupathi; Suresh Kumar P, Erode Sengunthar Engineering College, Thudupathi; Senthilkumar P, Erode Sengunthar Engineering College, Thudupathi


ANFIS, Control Valve, DAMADICS, Fault Detection, Fault Isolation, Dependent Parameters


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. This paper proposes a new integrated diagnostic system for pneumatic control valve fault diagnosis by means of a neuro fuzzy approach. 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 recognized by an Adaptive Neuro Fuzzy Inference System (ANFIS). 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 Adaptive Neuro Fuzzy Inference System has the ability to detect and identify various magnitudes of the faults and can isolate multiple faults.

Other Details

Paper ID: IJSRDV4I100411
Published in: Volume : 4, Issue : 10
Publication Date: 01/01/2017
Page(s): 843-847

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