Aircraft engine main bearing fault feature extraction method based on threshold parameter decision screening
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摘要:
针对航空发动机中滚动轴承微弱故障信号受环境噪声影响提取困难的问题，提出一种基于阈值参数判决筛选的航空发动机主轴承故障特征提取方法。为了自适应选择变分模态分解（variational mode decomposition，VMD）中的参数，采用粒子群算法（PSO）对VMD算法中的参数进行优化，将其作为前置参数来处理传感器收集到的轴承原始振动信号，得到
K 0个模态分量；其次提出一种新的参数调和公式，该公式将峭度和相关系数平衡融合为一个参数P ，然后基于阈值参数准则划分筛选出高信噪比信号，整合高信噪比信号产生新的振动信号；最后通过包络谱提取出轴承微弱故障特征。结果表明：参数调和公式与阈值参数判决方法能平衡峭度和相关系数之间的关系，滤除了峭度值较高但有效信息少的分量，该方法可有效提取滚动轴承简单及复杂传递路径下的故障特征，为航空发动机主轴承故障复杂信号处理和诊断提供了有效手段。Abstract:In view of the difficulty in extracting weak fault signals from rolling bearings in aircraft engines due to the influence of environmental noise, this paper proposes an aircraft engine main bearing fault feature extraction method based on threshold parameter decision screening. To adaptively select the parameters in variational mode decomposition (VMD), the Particle Swarm Optimization (PSO) algorithm is used to optimize the parameters in the VMD algorithm, which are used as pre-parameters to process the raw vibration signals collected by sensors, resulting in K0 modal components. Furthermore, a new parameter harmonization formula is proposed. This formula balances kurtosis and correlation coefficient and combines them into a single parameter P. Then, based on threshold parameter criteria, high signal-to-noise ratio signals are selected and integrated to generate new vibration signals. Finally, weak fault features of the bearing are extracted using envelope spectrum analysis. The results show that Parameter harmonic formula and threshold parameter decision method can balance the relationship between kurtosis and correlation coefficient, and filter out the components with higher kurtosis value but less effective information, the method can effectively extract fault features of rolling bearings in both simple and complex transmission paths, providing an effective means for complex signal processing and diagnosis of main bearing faults of aircraft engines.
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Key words:
- rolling bearings /
- VMD /
- parameter harmonization formula /
- threshold parameter /
- fault diagnosing /
- aircraft engine
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图 3 加入白噪声后仿真信号时域图
Figure 3. Time domain diagram of simulation signal after adding white noise
图 4 原始信号与去噪信号的时域波形
Figure 4. Time domain waveform of original signal and denoised signal
图 5 内圈故障仿真信号重构的包络谱
Figure 5. Envelope spectrum of inner ring fault simulation signal reconstruction
图 6 内圈故障仿真信号重构的局部包络谱
Figure 6. Local envelope spectrum of inner circle fault simulation signal reconstruction
图 8 轴承外圈故障原始信号的频域波形
Figure 8. Frequency domain waveform of original signal of bearing outer ring fault
图 13 试验台结构及故障信号传递路径示意图
Figure 13. Schematic diagram of test bed structure and fault signal transmission path
图 16 故障轴承原始信号的时域波形
Figure 16. Time domain waveform of the original signal of the faulty bearing
图 17 故障轴承原始信号的频域波形
Figure 17. Frequency domain waveform of the original signal of the faulty bearing
图 18 原始信号与去噪信号的时域波形
Figure 18. Time domain waveform of original signal and denoised signal
图 19 轴承滚动体故障重构信号包络谱（本文方法）
Figure 19. Envelope spectrum of fault reconstructed signal of bearing roller （Textual method）
图 20 峭度值筛选重构信号包络谱图
Figure 20. kurtosis value screening reconstruction signal envelope spectrum
图 22 原始信号与去噪信号的时域波形
Figure 22. Time domain waveform of original signal and denoised signal
图 23 轴承内圈故障重构信号包络谱（本文方法）
Figure 23. Envelope spectrum of bearing inner ring fault reconstruction signal （textual method）
图 25 轴承外圈故障重构信号包络谱（本文方法）
Figure 25. Envelope spectrum of bearing outer ring fault reconstruction signal （textual method）
表 1 高/低信噪比信号划分
Table 1. High/low SNR signal division
参数对比 高/低信噪比信号划分 P≥M 高信噪比信号 P<M 低信噪比信号 
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表 2 试验轴承参数
Table 2. Experimental bearing parameter
滚动体个数
Zr/个滚动体节径
Dr/mm滚动体直径
dr/mm接触角
α/（°）11 33.5 7 0
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表 4 试验轴承参数
Table 4. Test bearing parameter
类型 滚动体
个数/个外圈直径/
mm内圈直径/
mm接触角/
/（°）滚动体直径/
mm滚棒轴承 34 140 110 0 8
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表 5 滚动体故障各分量参数
Table 5. Parameters of rolling element fault components
IMF P M 1 2.102 1.405 2 2.188 3 0.953 4 1.23 5 1.415 6 1.187 7 1.641
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