Diagnosis of process valve actuator faults using a multilayer neural network

This paper investigates the ability of a multilayer neural network to diagnose actuator faults in a Fisher-Rosemount 667 process control valve. A software package that comes with the valve is used to obtain experimental figures of merit related to the position response of the valve given a step command. The particular values of the dead time, peak time, percent overshoot, steady state error, 63% and 86% rise times, and gain are shown to depend on the severity of three commonly occurring faults: incorrect supply pressure, actuator vent blockage, and diaphragm leakage. The relationships between these parameters form fault signatures for each operating condition that are subsequently learned by a multilayer feedforward neural network. The results show that the trained network has the capability to detect and identify various magnitudes of the faults of interest. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training. The approach presented in this paper allows the existing instrumentation to be utilised without modification. Thus, the proposed methodology is practical to implement.

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