A neural network based fault detection and identification scheme for pneumatic process control valves

This paper outlines a method for detection and identification of actuator faults in a pneumatic process control valve using a neural network. First, the valve signature and dynamic error band tests, used by specialists to determine valve performance parameters, are carried out for a number of faulty operating conditions. A commercially available software package is used to carry out the diagnostic tests, thus eliminating the need for additional instrumentation of the valve. Next, the experimentally determined valve performance parameters are used to train a multilayer feedforward network to successfully detect and identify incorrect supply pressure, actuator vent blockage, and diaphragm leakage faults