Fault Diagnosis of the Pneumatic Actuators Based on Neural Network

This paper investigates the ability of a multilayer neural network to diagnose pneumatic actuator faults. The AMS Valve Link Software [1]is used to obtain experimental figures of merit related to the actuator. The particular values of six measurable quantities are shown to depend on the severity of commonly occurring faults: diaphragm leakage, actuator vent blockage and valve clogging. The relationships between these parameters from fault signatures for each operating condition that are subsequently learned by a multilayer neural network. Through the simulation research based on MATLAB/Simulink, the results show that the trained network has the ability to estimate fault levels not seen by the network during training.