Acoustic diagnosis for compressor with hybrid neural network

Abstract

Describes an acoustic diagnosis technique for a compressor using a hybrid neural network (HNN). The HNN is composed of two neural networks: an acoustic feature extraction network, and a fault discrimination network. The acoustic feature extraction network uses an auto-associative neural network (ANN) whose target patterns are the same as the input patterns. The five-layered neural network is composed of two three-layered neural networks to compress the input information and to restore the compressed information. The authors examine the architecture of the ANN for acoustic diagnosis, the proper form of the activation function in the output layer and the proper number of hidden layers. The fault discrimination network uses a multilayered neural network whose input patterns are the output values of the hidden layer in the ANN. The authors examine the possibility of discriminating between eight types of compressor faults with high accuracy by using an HNN. >