基于高阶谱和Tamura纹理的滚动轴承故障诊断
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武汉科技大学 信息科学与工程学院

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中图分类号:

TH133.33;TP277

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Fault Diagnosis of Rolling Bearing Based on High-order Spectrum and Tamura Texture
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School of Information Science and Engineering,Wuhan University of Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    为了提高滚动轴承的故障诊断率,提出了一种基于高阶谱(high order spectrum, HOS)和Tamura纹理特征相结合的故障诊断方法。首先,通过高阶谱方法将滚动轴承故障振动信号的冲击提取出来;然后,对高阶谱进行处理得到二维等高线图;最后依据轴承故障相同时等高线图具有相似性以及不同时具有差异性这一特性,采用基于人类视觉感知的Tamura纹理描述方法提取特征参数后输入到多分类支持向量机(support vector machines, SVM)中进行分类。结果表明:高阶谱结合Tamura纹理特征的滚动轴承故障诊断方法在较少特征参数下故障识别准确率能达到较高的精度,对于故障尺寸不同的混合振动信号识别准确率稳定,诊断效果良好。

    Abstract:

    In order to improve the fault diagnosis rate of rolling bearings, a fault diagnosis method based on the combination of high-order spectrum(HOS) and Tamura texture features is proposed. First, the impact of the fault vibration signal of the rolling bearing is extracted by the high-order spectrum method. Then, the high-order spectrum is processed to obtain a two-dimensional contour map. According to the characteristics that the contour maps are similar when the bearing faults are the same and different at the same time, the Tamura texture description method based on human visual perception is used to extract features. The parameters are then input into a multi-class support vector machine(SVM) for classification. The results show that the high-order spectrum combined with the Tamura texture feature of the rolling bearing fault diagnosis method can achieve high fault identification accuracy with fewer characteristic parameters, and the identification accuracy of mixed vibration signals with different fault sizes is stable and the diagnosis effect is good.

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陆翔宇,周凤星,严保康,等. 基于高阶谱和Tamura纹理的滚动轴承故障诊断[J]. 科学技术与工程, , ():

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历史
  • 收稿日期:2021-11-16
  • 最后修改日期:2022-04-25
  • 录用日期:2022-04-30
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