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国外文献 > 1.轴承 > 4. 设计与分析 > 算法与优化

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标 题： A Vibration Signal Filtering Method Based on KL Divergence Genetic Algorithm: with Application to Low Speed Bearing Fault Diagnosis
基于KL散度的遗传算法振动信号滤波方法：应用于低速轴承故障诊断
作 者：
关键词： Kullback-Liebler divergence  [库尔巴克-利布勒测度]
Genetic algorithm  [ 遗传算法]
Vibration signal filtering  [ 振动信号滤波]
Bearing fault diagnosis  [ 轴承故障诊断]
出版年： 2018
会议名： 23rd IEEE International Conference on Digital Signal Processing (DSP 2018)
第23届IEEE国际数字信号处理会议（DSP 2018）
届 次： 23rd
摘 要： Fault signal plays a crucial role in bearing fault detection and diagnosis. However, in some low speed condition, the fault signal is weak and overwhelmed by the strong noise. It is hard to extract fault signal information effectively. To handle this problem, a vibration signal filtering method based on Kullback-Liebler (KL) divergence genetic algorithm (GA) is presented. This method employs binary string of GA to match the frequency of fault signal frequency spectrum. Based on the KL divergence as GA fitness function, the signal similarity between normal condition and the KL divergence GA filtered signal is evaluated and GA employs genetic operations to obtain the optimal noise binary string. After that, negating this optimal noise binary string can obtain optimal fault signal binary string, which will then be multiplied by diagnosis vibration signal frequency spectrum, to yield a new frequency spectrum that only has fault components. Based on envelope demodulation method and bearing fault characteristic frequency, the effective and advanced performance of the proposed method is evaluated by three different rolling bearing faults in low speed bearing vibration signal.

故障信号在轴承故障的检测与诊断中起着关键作用。然而，在某些低速工况下，故障信号较弱，容易被强噪声淹没，难以有效提取故障信号信息。为解决这一问题，本文提出一种基于Kullback-Liebler（KL）差异遗传算法（GA）的振动信号滤波方法。该方法利用GA的二进制字符串匹配故障信号频谱的频率特征。以KL差异作为GA的适应度函数，评估正常状态与经KL差异GA滤波后的信号之间的相似性，然后通过遗传操作获取最优的噪声二进制字符串。接着，对这个最优噪声二进制字符串取反，即可得到最优的故障信号二进制字符串，并将其与诊断振动信号的频谱相乘，从而生成仅包含故障分量的新频谱。基于包络解调法和轴承故障特征频率，本文通过三种不同滚动轴承故障在低速轴承振动信号中的应用，对所提方法的有效性和先进性进行了评估。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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