噪声与振动控制Issue(3)：173-176,4.DOI:10.3969/j.issn.1006-1335.2015.03.037
基于约束独立成分分析的轴承复合故障特征提取方法
Application of CICA in Compound Fault Feature Extracting of Rolling Bearings
摘要
Abstract
In order to extract fault features from compound signals, a method based on discrete wavelet transform (DWT) and constrained independent component analysis (CICA) was proposed. In this method, the single channel vibration signal was decomposed into several wavelet coefficients by DWT method, and the wavelet re-construction function was used to reconstruct the decomposed signal. Then, envelope signals of the reconstructed wavelet coefficients were selected as the input matrix of CICA algorithm, and the reference signal was established based on prior knowledge of source signals. Finally, the fault signals were separated and the fault features were extracted. Experimental results validated the effectiveness of the proposed method in compound fault separating and diagnosis of rolling bearings.关键词
振动与波/复合故障诊断/约束独立成分分析/离散小波变换/滚动轴承Key words
vibration and wave/compound fault diagnosis/constrained independent component analysis (CICA)/discrete wavelet transform (DWT)/rolling bearing分类
机械制造引用本文复制引用
李瑞彤,王华庆,屈红伟,齐放,李美娇..基于约束独立成分分析的轴承复合故障特征提取方法[J].噪声与振动控制,2015,(3):173-176,4.基金项目
国家自然科学基金项目 （）
