In the field of system health management, the quality of rolling equipment is very important. Therefore, the fault diagnosis of rolling bearings has become a hot research topic. In this paper, the traditional fault feature extraction method is used to optimize the non-linear and non-stationary characteristics of the bearing vibration signal. Furthermore, in order to improve the performance of the fault diagnosis, a novel signal fingerprint is proposed to recognize the fault type. The simulation result show that the new method is successful and effective, and the recognition rate can be improved up to 95.33%, which is better than the traditional methods.

中文翻译：

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滚动轴承故障特征提取与识别研究

在系统健康管理领域，轧制设备的质量非常重要。因此，滚动轴承的故障诊断已成为研究的热点。本文采用传统的故障特征提取方法来优化轴承振动信号的非线性和非平稳特性。此外，为了提高故障诊断的性能，提出了一种新颖的信号指纹识别故障类型。仿真结果表明，该方法是成功有效的，识别率可以提高到95.33％，优于传统方法。