Hybrid neural network based fault diagnosis of rotating machinery

Vibration fault is the main fault of hydraulic generator set. From the analysis of vibration signal, it provides a wealthy of information for fault diagnosis. This paper presents a hybrid approach of neural network to realize automatic diagnosis. Pulse coupled neural network (PCNN) has very strong capability in the feature extraction, and entropy time signature from a PCNN has the property of insensitive to rotation, scaling and translation, it is used to extract the feature vector of vibration signal. Probability neural network (PNN) has excellent performance in the pattern recognition. Therefore, it is used in the vibration fault classification. Experimental results show the proposed method greatly robust to diagnose the fault, by comparison with another artificial neural network.