基于变分贝叶斯理论的机械故障源盲分离方法研究  

Blind separation of the mechanical fault sources based on variational bayesian theory

作　　者：李志农[1] 范涛[1] 刘立州[1] 卢纪富[2] 

机构地区：[1]郑州大学机械工程学院,郑州450001 [2]郑州大学土木工程学院,郑州450001

出　　处：《振动与冲击》2009年第6期12-16,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金(50775208);河南省教育厅自然科学基金(2006460005;2008C460003)

摘　　要：未知噪声环境下机械源信号盲分离方法由于忽略噪声影响往往得到很差的分离效果。针对此问题,提出了一种基于变分贝叶斯独立分量分析的机械故障源分离方法,该方法与传统的机械源分离方法相比,具有以下独特特点,即不需要将未知噪声看成一种独立源,也不需要进行消噪预处理,可直接对噪声干扰的机械源信号进行有效分离。仿真研究表明,提出的方法优于传统的机械源分离方法,分离误差大幅度降低。实验结果也验证了所提出方法的有效性。Considering the deficiency of the traditional blind seperation methods under the condition of unknown noises, a new separation method for machine fault sources based on variational Bayesian independent component analysis was proposed. Compared with the traditional method, the proposed method has such unique characteristics: it is unnecessary to regard the unknown noise as an independent source, the denoising preprocessing can be cancelled, and the mechanical sources under the noisy environment can be directly separated. The simulation results show that the proposed method is superior to the traditional method, with the separation error greatly reduced. The experiment results also verify the validity of the proposed method.

关 键 词：盲源分离 故障诊断 变分贝叶斯 独立分量分析 

分 类 号：TP227[自动化与计算机技术—检测技术与自动化装置] TH17[自动化与计算机技术—控制科学与工程]