DEVELOPMENT OF CONTROL VALVE STICTION DETECTION METHOD USING NEURAL NETWORK (NN)

Control valve stiction can be considered as one the primary causes that can affect a control system performance. Since control valve act as the final part of control element, it can cause disturbances towards an operation. In 1989, an initiative has been created where a valve stiction detection method is technologically advanced to detect stiction in a control valve. Afterwards, numerous methods are made and redeveloped to improvise the technique of detection the stiction in a control valve. However, none of the methods so far are using the neural network methods. Therefore, this project will cover on several neural network methods on valve stiction detection and will be tested for the effectiveness in detecting the fault. This project will be conducted by using MATLAB© Simulink to compute the simulation model. The initial objective of the design is to determine the number of neurons and the type of transfer function used in the hidden layer for the feedforward neural network model in this project. The outcome of the feed forward model use 11 units in the hidden layer, as the result produced is favorable by using 11 neurons compared to different number of neurons. As for the transfer function, the feedforward model uses tansig as the transfer function in the hidden layer and purelin as the transfer function in the output layer. As for the neural network strategy, three approaches are tested and analyzed. First approach and third approach produced unfavorable result. However, second approach produced effective and favorable result but still unpractical to be applied due to high amount of error generated.