Neural network classifiers applied to condition monitoring of a pneumatic process valve actuator

As modern process plants become more complex, the ability to detect and identify the faulty operation of pneumatic control valves is becoming increasingly important. In this work, a neural network pattern classifier is employed to carry out fault diagnosis and identification upon the actuator of a Fisher-Rosemount 667 industrial process valve. The network is trained with experimental data obtained directly from a software package that comes with the valve. This has eliminated the need for additional instrumentation of the valve. Using this software, tests are carried out to obtain experimental parameters associated with the valve performance for incorrect supply pressure, diaphragm leakage, and vent blockage faults. Specifically, the valve signature and dynamic error band tests are used to directly obtain lower and upper bench sets, minimum, maximum, and average dynamic errors, as well as the dynamic linearity. Additionally, valve deadband and hysteresis are measured graphically from the available valve signature plots for each faulty condition. The relationships between these parameters, for each fault, form signatures that are subsequently learned by a multilayer feedforward network trained by error back-propagation. The test results show that the resulting network has the ability to detect and identify various magnitudes of each fault. It is also observed that a smaller network with a shorter training time results when the valve deadband and hysteresis are included in the training data. Thus, the extra effort required to extract these parameters from the valve signature plots is justified. (C) 2002 Elsevier Science Ltd. All rights reserved.