Fault classification of fluid power systems using a dynamics feature extraction technique and neural networks

Abstract

Multilayer perceptron (MLP) type neural networks and dynamic feature extraction techniques, namely linear prediction coding (LPC) and LPC cepstrum, are used to classify leakage type and to predict leakage flowrate magnitude in an electrohydraulic cylinder drive. Both single-leakage and multiple-leakage type faults are considered. A novel feature is that only pressure transient responses are employed as information. In addition, the feature extraction technique used to detect faults can result in a large data dimensionality reduction. The performance of two MLP models, namely serial and parallel, are studied to reflect the importance of the way data are presented to the MLP.