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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 Novel Model of one-class Bearing Fault Detection using SVDD and Genetic Algorithm
基于支持向量数据降维与遗传算法的一类轴承故障检测新模型
作 者： Tao Xin-min
Chen Wan-Hai
Du Bao-Xiang
XuYong
Dong Han-Guang
关键词： Fault detection  [故障检测]
Genetic algorithm  [ 遗传算法]
Support vector data description  [ 支持向量数据描述]
Kernel parameters  [ 内核参数]
Rotating machines  [ 旋转机械]
出版年： 2007
会议名： 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007), vol.2
第二届IEEE工业电子与应用会议（ICIEA 2007），第2卷
届 次： 2nd
摘 要： In many bearing fault anomaly detection application, only positive (normal) samples are available for training purposes, other abnormal samples are difficult to be available. In order to solve these practical application problems, a novel model of one-class bearing fault detection based on SVDD and Genetic Algorithm is presented in this paper. The time domain statistics features are processed as inputs to SVDD for one-class (normal) recognition. Then SVDD is used to describe the normal data distribution characteristics with high data description ability. The SVDD is trained only with a subset of normal samples. This paper also analyzes the behavior of the classifier based on parameter selection and proposes a novel way based on genetic algorithm to determine the optimal threshold parameters. The hybrid one-class classification model of SVDD and Genetic Algorithm is determined to address the problem of difficultly collecting abnormal samples in bearing fault detection. Comparison of the performance of detection of SVDD with different kernel parameters is experimented. This hybrid approach is compared against other MLP detection techniques. The results show the relative effectiveness of the proposed classifiers in detection of the bearing condition with some concluding remarks.

在许多轴承故障异常检测应用中，仅能获得正样本（正常样本）用于训练，而其他异常样本则难以获取。为解决这些实际应用中的问题，本文提出了一种基于支持向量数据依赖分布（SVDD）和遗传算法的一类分类轴承故障检测模型。将时域统计特征作为输入，通过SVDD进行一类（正常）识别。随后，利用SVDD对正常数据的分布特性进行描述，其具备较强的特征描述能力。SVDD仅使用正常样本的一个子集进行训练。本文还分析了分类器在参数选择过程中的行为，并提出一种基于遗传算法的新方法来确定最优阈值参数。该混合的一类分类模型结合了SVDD与遗传算法，有效解决了轴承故障检测中异常样本采集困难的问题。实验对比了不同核函数参数下SVDD的检测性能，并将该混合方法与其他多层感知机（MLP）检测技术进行了比较。结果表明了所提出分类器在检测轴承状态方面的相对有效性，并附有若干结论性 remarks。
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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