无转速阶次跟踪的滚动轴承故障诊断方法

韩佳霖; 高宏力; 郭亮; 刘学; 吴婷婷; 李世超

doi:10.3969/j.issn.0258-2724.20220116

无转速阶次跟踪的滚动轴承故障诊断方法

doi: 10.3969/j.issn.0258-2724.20220116

韩佳霖^{1, 2,},
高宏力^1, ,,
郭亮¹,
刘学^{1, 2},
吴婷婷¹,
李世超¹

1.
西南交通大学机械工程学院，四川成都 610031
2.
中国铁建重工集团股份有限公司，湖南长沙 410100

基金项目: 国家自然科学基金（51775452）；四川省科技计划（2021YFG0068）

详细信息

作者简介:
韩佳霖（1987—），男，高级工程师，研究方向为设备智能化状态监测与故障诊断技术，E-mail： jialinhan@126.com

通讯作者:
高宏力（1971—），男，教授，博导，研究方向为设备故障预警与健康管理， E-mail：hongli_gao@swjtu.edu.cn

中图分类号: TP277；TH117.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-02-15
- 修回日期: 2022-07-04
- 网络出版日期: 2023-01-13
- 刊出日期: 2022-08-05

Bearing Fault Diagnosis Method Based on Order Tracking Without Rotational Speed

HAN Jialin^{1, 2
,},
GAO Hongli^{1
, ,},
GUO Liang¹,
LIU Xue^{1, 2},
WU Tingting¹,
LI Shichao¹

1.
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
China Railway Construction Heavy Industry Corporation Limted, Changsha 410100, China

摘要

摘要:
机械装备轴承运行速度多变导致振动监测信号频谱模糊，一定程度影响了轴承故障诊断的准确性. 目前的无转速阶次跟踪技术在转速波动较小且转频谐波不重叠时效果较好，但当轴承转频谐波重叠时则难以进行分析. 针对以上问题，提出了一种基于广义解调的无转速阶次跟踪轴承故障诊断方法. 首先，采用广义傅里叶变换和改进的基于成本函数脊线提取技术准确提取出轴承的转频谐波分量；同时，采用快速谱峭度算法和带通滤波器对轴承振动信号进行去噪处理；然后，通过角度重采样将去噪后的时域信号转换为角域信号；最后，通过包络谱分析得到轴承的阶次谱信息，从而识别轴承的故障类型. 研究结果表明：通过数值模拟信号和实际轴承监测信号验证了提出方法的有效性，通过与传统包络谱方法对比可知，提出的方法可进行无转速信息时的滚动轴承阶次跟踪故障诊断.
- 阶次跟踪 /
- 故障诊断 /
- 广义傅里叶变换 /
- 成本函数
Abstract:
The variable operating speed of mechanical equipment bearings leads to blurred spectrum of vibration monitoring signals, which to some extent affects the accuracy of bearing fault diagnosis. The current tacholess order tracking technology works well when the rotational speed fluctuation is small and the rotational frequency harmonics do not overlap, but it is difficult to conduct analysis when the bearing rotational frequency harmonics overlap. Aiming at the above problems, this paper proposes a tacholess order tracking fault diagnosis method of bearings based on the generalized demodulation. First, the generalized Fourier transform and the improved cost function-based ridge extraction technology are used to accurately extract the rotational frequency harmonic components of the bearings. At the same time, the fast spectral kurtosis algorithm and a band-pass filter are used to de-noise the bearing vibration signal. Then, the denoised time-domain signal is converted into an angular-domain signal through angle resampling. Finally, the order spectrum information of the bearing is obtained through the envelope spectrum analysis, so as to identify the fault type of the bearing. In addition, the effectiveness of the proposed method is verified using the numerical simulation signal and the actual bearing monitoring signal. The results show that the proposed method has an error of less than 5% in reconstructing the phase and an accuracy of more than 94% in characterizing the order frequency of bearing faults, which can be used for the order tracking fault diagnosis of rolling bearings without rotational speed information.
- order tracking /
- fault diagnosis /
- generalized Fourier transform /
- cost function

HTML全文

图 1 无转速阶次跟踪轴承故障诊断算法流程

Figure 1. Flow chart of bearing fault diagnosis algorithm based on order tracking without rotational speed

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图 2 广义解调算法

Figure 2. Generalized demodulation algorithm

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图 3 数值模拟信号

Figure 3. Numerical simulation signals

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图 4 数值模拟信号时频分布

Figure 4. Time-frequency distribution map of the numerical simulation signal

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图 5 脊线提取

Figure 5. Ridge extraction

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图 6 广义解调算法处理图

Figure 6. Processing diagram of the generalized demodulation algorithm

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图 7 瞬时相位

Figure 7. Instantaneous phases

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图 8 快速谱峭度

Figure 8. Fast spectral kurtosis

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图 9 原始信号带通滤波

Figure 9. Band-pass filtering of the original signal

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图 10 包络阶次分析

Figure 10. Envelope order analysis

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图 11 传统包络谱

Figure 11. Traditional envelope spectrum

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图 12 试验台

Figure 12. Experimental platform

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图 13 振动信号时频分析

Figure 13. Time-frequency analysis of the vibration signal

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图 14 脊线提取

Figure 14. Ridge extraction

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图 15 广义解调算法处理

Figure 15. Processing diagram of the generalized demodulation algorithm

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图 17 快速谱峭度

Figure 17. Fast spectral kurtosis

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图 18 原始振动信号滤波

Figure 18. Band-pass filtering of the original signal

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图 16 瞬时相位

Figure 16. Instantaneous phases

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图 19 包络阶次分析

Figure 19. Envelope order analysis

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图 20 传统包络谱

Figure 20. Traditional envelope spectrum

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