Fault diagnosis based on pulse coupled neural network and probability neural network

In operation of mechanical equipment, fault diagnosis plays an important role. In this paper, a novel fault diagnosis method based on pulse coupled neural network (PCNN) and probability neural network (PNN) is presented. The shape information of shaft orbit provides an important basis for fault diagnosis. However, the feature extraction and classification of shaft orbit is difficult to realize automation. The PCNN technique has excellent performance in the feature extraction. In the present study, a PCNN combined with roundness method is used to extract the feature vector of shaft orbit, because time signature from a PCNN has the property of insensitive to rotation, scaling and translation. Meanwhile, roundness is also with the same properties. Further, the PNN is used to train the feature vectors and classify the vibration fault. By comparison with the back-propagation (BP) network and radial-basic function (RBF) network, the experimental result indicated the proposed approach achieved fast and efficient fault diagnosis.

Highlight
► Pulse coupled neural network and probability neural network are used to diagnose the mechanical equipment fault. PCNN combined with roundness is used to extract the feature vector of shaft orbit. PNN is used to classify the fault. The method achieved fast and efficient fault diagnosis.