关键词: 快速独立分量分析, 故障诊断, 轴承故障, 遗传算法

Abstract:

Depending on the intrinsic weakness and advantages of back propagation(BP) neural network and Fast Independent Component Analysis(FastICA), a Fast Independent Component Analysis(FastICA) Genetic Neural Networks Method was proposed for fault characteristic signal recognition. The FastICA is used to decompose signals to obtain the independent components successively, each of Independent components corresponding to an energy band, and feature vector of each energy band is used as input sample to optimize neural network. Secondly, the genetic algorithm is used to optimize the weights and thresholds of BP neural network to obtain the Genetic Neural Network. Thirdly,the feature vector is used as input sample of the genetic neural network to identify the fault. Using this method can analysis and identify many kinds of rolling bearings fault signal, and through this method the ability of fault identification isimproved.

Key words: fast independent component analysis, fault diagnosis, bearing fault, genetic algorithm