Neural network detection and identification of actuator faults in a pneumatic process control valve

This paper establishes a scheme for detection and identification of actuator faults in a pneumatic process control valve using neural networks. First, experimental performance parameters related to the valve step responses, including dead time, rise time, overshoot, and the steady state error are obtained directly from a commercially available software package for a variety of faulty operating conditions. Acquiring training data in this way has eliminated the need for additional instrumentation of the valve. Next, the experimentally determined performance parameters are used to train a multilayer perceptron network to detect and identify incorrect supply pressure, actuator vent blockage and diaphragm leakage faults. The scheme presented here is novel in that it demonstrates that a pattern recognition approach to fault detection and identification, for pneumatic process control valves, using features of the valve step response alone, is possible.