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统计

国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A new approach to detection of defects in rolling element bearings based on statistical pattern recognition
基于统计模式识别的滚动轴承缺陷检测新方法
作 者： Pavle Stepanic
Ilija V. Latinovic
Zeljko Djurovic
关键词： Feature extraction  [特征提取]
Statistical parameters  [ 统计参数]
Fault characteristic frequencies  [ 故障特征频率]
Envelope analysis method  [ 信封分析法]
Dimension reduction  [ 降维]
Statistical pattern recognition  [ 统计模式识别]
出版年： 2009
期刊名： The International Journal of Advanced Manufacturing Technology
先进制造技术国际期刊
年卷期： 2009, vol.45, no.1/2
摘 要： The paper presents a new approach to the classification of rolling element bearing faults by implementing statistical pattern recognition. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is performed by a combination of signal's time-varying statistical parameters and characteristic rolling element bearing fault frequency components obtained through the envelope analysis method. In this way, an 18-dimensional vector of the vibration signal feature is obtained. Dimension reduction of the 18-dimensional feature vectors was performed afterward into two-dimensional vectors representing the training set for the design of parameter classifiers. The classification was performed in two classes, into defective and functional rolling element bearings. Main trait of parameter classifiers is simplicity in their design process, as opposed to classifiers based on neural networks, which employ complex training algorithms.

本文提出了一种基于统计模式识别的滚动轴承故障分类新方法。滚动轴承故障的诊断本质上是模式分类与识别问题，其中关键步骤是从振动信号中提取特征。通过结合信号的时间变化统计参数以及利用包络分析法获得的典型滚动轴承故障频率分量，对每条记录的振动信号进行表征。由此得到一个包含18个维度的振动信号特征向量。随后，对这18维特征向量进行降维处理，得到二维向量，作为参数分类器设计的训练集。分类分为两个类别：有缺陷和正常功能的滚动轴承。参数分类器的主要优点在于其设计过程简单，而基于神经网络的分类器则需要使用复杂的训练算法。
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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