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A two-step blind source extraction method and its application in fault diagnosis of rolling element bearing

机译:两步盲源提取方法及其在滚动元件轴承故障诊断中的应用

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摘要

The vibration signals will take on cyclical characteristics when fault arises in the rolling element bearing, and a two-step blind source extraction (BSE) method for fault diagnosis of rolling element bearing is proposed in the paper using the above property. Firstly, calculate the theoretical basic cyclet of the target source fault signal, and the weighted separation matrix w and desired source signal are obtained coarsely. Secondly, use w as the initial weighted matrix and apply the fixed-point algorithm basing on high-order statistics on the observed signals, and much more perfect target source signal is got at last. The proposed method has the following advantages over other BSE method such as constrained independent component analysis (CICA) basing on the analyzed results of simulation and experiment: The fundamental period of the target source signal does not needed to be estimated accurately, and the reference signal also does not need to be constructed precisely. However, these two conditions are the necessary prerequisites of CICA. Besides, the proposed method also has the advantage of higher accuracy over the other recent BSE methods through comparison.
机译:当滚动元件轴承中出现故障时,振动信号将采用循环特性,并且使用上述性质在纸上提出了用于滚动元件轴承的故障诊断的两步盲源提取(BSE)方法。首先,计算目标源故障信号的理论基本循环,并且粗略地获得加权分离矩阵W和期望的源信号。其次,使用W作为初始加权矩阵,并应用于观察信号的高阶统计数据的定点算法,并且终于获得了更完美的目标源信号。该方法具有以下优于其他BSE方法,例如受约束的独立分量分析(CICA),基于模拟和实验的分析结果:目标源信号的基本周期不需要准确地估计,以及参考信号也不需要精确构建。然而,这两个条件是CICA的必要先决条件。此外,所提出的方法还通过比较具有对其他最近BSE方法的更高精度的优点。

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