A fault classification approach is presented which considers the advantages of Time Encoded Signal Processing (TESP) of dynamic signals combined with the ability of Artificial Neural Networks (ANNs) to classify changes in TESP codes. This is demonstrated using a new TESP code approach applied to a pressure control system exhibiting both leakage at the actuator and a servovalve fault. It was found that the use of both pressure transducer voltage and servovalve drive voltage, when entered into the ANN in a parallel data structure manner, resulted in an excellent fault classification capability. In addition the inherent classification approach gave very good leakage discrimination for arbitrarily-set, and low, levels of 0, 2, 4, 6 l/min. A range of 16 different ANNs were investigated and the classification results indicate a preferred topology for this application.
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