文档介绍：摘 要
滚动轴承是旋转机械中使用最为广泛和最易受损的零部件之一, 其工作状态直接影响到整个机械系统的性能,对其进行故障诊断具有重要的实际意义。
基于振动信号分析的滚动轴承特征提取方法是国内外使用最多、也是最有效的方法之一。统计分布模型参数在可靠性工程中已被广泛应用于反映机械产品的疲劳寿命和疲劳强度,但在机械特别是轴承状态监测和故障诊断中用于特征提取的研究尚未多见。为了充分挖掘滚动轴承运行中蕴含的有效状态变化信息,提出了一种基于威布尔分布模型参数及其数字特征的故障特征提取新方法。在对滚动轴承原始振动信号建立 Weibull 分布模型的基础上,分别提取模型的尺度参数以及中位数作为表征轴承运行状态的一种新特征向量。仿真试验结果证明了该特征提取方法的有效性。
针对滚动轴承振动信号的非高斯特性,提出了一种基于对数正态分布模型的故障特征提取新思路,提取其模型参数的对数均值作为表征滚动轴承运行状态的新特征量。有效地解决了振动信号的非高斯问题。
针对上述方法无法全面准确描述滚动轴承振动信号的非平稳特性问题,提出了一种基于小波域对数正态模型的滚动轴承故障特征提取新方法。首先,对滚动轴承振动信号进行小波、小波包分析,将非平稳信号转化为平稳信号,在平稳信号的基础上建立典型的非高斯分布模型—对数正态分布模型,最后提取每个尺度上的对数正态分布模型参数作为表征轴承运行状态的新特征量。试验证明了所提特征提取方法有效地解决了滚动轴承振动信号的非平稳、非高斯问题。
关键词:特征提取,威布尔分布,对数正态分布,小波域,滚动轴承
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ABSTRACT
The rolling bearing is one of the most widely used and damageable parts in rotating machinery with its working state directly impacting on the performance of the whole machine, and the fault diagnosis of rolling bearing has a very important practical significance.
The fault diagnosis of rolling bearing based on vibration signal analysis is one of the monly used and effectively statistical distribution model parameters are widely used for characterizing the fatigue life and strength of the mechanical products in the reliability engineering. But it has not been paid any attention to that they are used for feature extraction of fault information in the condition monitor and fault diagnosis of mechanism especially the rolling bearing. A novel approach to fault feature extraction based on Weibull distribution parameters is proposed, to mining fully the useful information of state change in the original signal of bearing vibration. After the original signal is modelled as the Weibull distribution, its scale parameter and digital feature-median is extracted as a new feature variable for the state of bearing running respectively. The tests results of fault diagnosis of the rolling bearing verify that this new feature variable is effective.
According to the problem that the rolling bearing vibratio