独立分量分析基网络应用于旋转机械故障特征抽取与分类

INDEPENDENT COMPONENT ANALYSIS BASED NETWORKS FOR FAULT FEATURES EXTRACTION AND CLASSIFICATION OF RATATING MACHINES

提出了一种新颖的、基于独立分量分析(ICA)的多层神经网络,用于旋转机械不同模式(如正常及轴承故障等)的特征抽取,随后利用多层感知器(MLP)实施最终的模式分类。借助独立分量分析方法,隐藏于多通道振动观测中的不变特征得到有效提取,从而建立起稳定的MLP分类器。试验所获得的成功分类结果表明,所建议的新的旋转机械健康状况监测方法具有较大的应用潜力。

A novel multi-layer neural networks is proposed, which is based on independent component analysis (ICA) for feature extraction of different modes (for example normal and bearing fault etc.), followed by a multi-layer perceptron (MLP) which implements the final classification. By the use of ICA, invariable features embedded in multi-channel vibration measurements can be captured. Thus, stable MLP classifier is constructed. The successful results achieved by the selected experiments indicated great potential of the new method in health condition monitoring of rotating machines.