基于机匣振动信号的滚动轴承故障协同诊断技术

林桐; 陈果; 滕春禹; 王云; 欧阳文理; 肖圣迪

doi:10.13224/j.cnki.jasp.2018.10.009

基于机匣振动信号的滚动轴承故障协同诊断技术

doi: 10.13224/j.cnki.jasp.2018.10.009

1.
南京航空航天大学民航学院,南京 211106
2.
中国航空工业集团有限公司中国航空综合技术研究所,北京 100028

基金项目: 国家自然科学基金面上项目(51675263)

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出版历程
- 收稿日期: 2017-06-29
- 刊出日期: 2018-10-28

Rolling bearing collaborative fault diagnosis technology for casing vibration signal

摘要

摘要: 针对基于机匣测点信号的航空发动机滚动轴承故障诊断问题,提出了一种滚动轴承故障的协同诊断技术。通过最小熵解卷积消除信号传递路径的影响以增强信号中的冲击性成分；通过小波变换提取共振频带；通过自相关分析抑制频带信号中的非周期性成分并进一步提升信噪比。依托带机匣的转子试验器分别对人工故障轴承和真实故障轴承进行了两组试验,试验结果表明:相比于其他典型方法,采用所提协同诊断法得到的包络谱中故障特征频率对应的谱峰更加清晰、明显。
- 协同诊断 /
- 滚动轴承 /
- 机匣信号 /
- 特征提取 /
- 最小熵解卷积 /
- 小波变换 /
- 自相关分析
Abstract: A cooperative diagnosis technique for rolling bearing faults was proposed for aero-engine rolling bearing fault diagnosis based on casing measuring point signal. Firstly, the minimum entropy deconvolution was used to eliminate the influence of the signal transmission path and enhance the impulsive component in the signal.Then,the resonance band was extracted by applying wavelet transform. Finally,the non-periodic signal components in the resonance band were suppressed by using autocorrelation analysis while the signal-to-noise ratio was further improved. Two bearing tests were carried out respectively on the artificial fault bearing and the real fault bearing on the rotor tester with casing. Test results showed that compared with other typical methods, the spectrum peaks corresponding to the fault characteristic frequencies in the envelope spectrum obtained by the proposed cooperative diagnosis method were more clear and obvious.
- collaborative diagnosis /
- rolling bearing /
- casing signal /
- feature extraction /
- minimum entropy deconvolution /
- wavelet transform /
- autocorrelation analysis

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参考文献(23)

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