Fault feature extraction method of aircraft engine rolling bearings based on comprehensive dynamic screening
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摘要:
针对航空发动机滚动轴承故障信号时常受高频宽幅的强背景噪声影响导致特征难以提取与表征的问题，提出一种新的滚动轴承故障特征提取方法。该方法首先对采集到的轴承振动信号进行小波包分解，计算得到各个节点分量的峭度值、相关系数值和能量比；然后以方差为指标，为不同振动信号找到最适合该信号的权重，将其融合为综合动态筛选指标；再计算各分量的贡献度，选取贡献度达到阈值的前
i 个node分量进行重构得到去噪后的新信号；最后通过包络谱提取轴承微弱故障特征。经仿真信号验证，去噪信号的信噪比相对于去噪前提升了8.82 dB。开展了某型航空发动机中介轴承故障模拟试验和某型涡扇航空发动机主轴承故障模拟试验，对所提方法分别进行了有效性验证。结果表明：该方法可准确提取航空发动机滚动轴承的故障特征频率及其倍频，进而实现故障诊断，经理论与试验验证，可作为航空发动机滚动轴承复杂信号处理和诊断的有效方法之一。Abstract:In view of the problem that the fault signal of aero-engine rolling bearing is often affected by high frequency and wide background noise, and the difficulty to extract and characterize the features, a new fault feature extraction method for rolling bearing was proposed. This method firstly performed wavelet packet decomposition on the bearing vibration signal and calculated the kurtosis value, correlation coefficient value, and energy ratio of each node component. Then using variance as an indicator, the most suitable weight for different vibration signals was found and integrated into comprehensive dynamic screening indicators. The contribution of each component was calculated. The first
i node components with contribution reaching the threshold for reconstruction was selected to obtain a denoised new signal. Finally, the weak fault features of bearings were extracted through envelope spectrum analysis. Through verification by simulation signals, the signal-to-noise ratio of the denoised signal increased by 8.82 dB compared with that before denoising. Simulated tests on the intermediate shaft bearing failure of a certain type of aircraft engine and the main shaft bearing failure of a certain type of turbofan aircraft engine were conducted, effectively validating the methods proposed. Results indicated that the method presented accurately extracted the fault feature frequencies and their harmonics of the rolling bearings in aircraft engines, thereby achieving fault diagnosis. Through theoretical analysis and test validation, it can be considered as one of the effective methods for complex signal processing and diagnosis of rolling bearings in aircraft engines. -
图 2 凯斯西储大学简单路径故障轴承模拟试验台
Figure 2. Simple path faulty bearing simulation test bench at Case Western Reserve University
图 3 经典试验台外圈重构包络谱
Figure 3. Outer ring reconstruction envelope spectrum on classic test bench
图 7 仿真信号外圈重构包络谱
Figure 7. Outer ring reconstruction envelope spectrum of simulated signal
图 17 综合筛选指标重构包络谱
Figure 17. Reconstruction envelope spectrum of comprehensive screening index
图 18 WPD-KVI-Hilbert内圈重构包络谱
Figure 18. WPD-KVI-Hilbert inner ring reconstruction envelope spectrum
图 21 轴承滚动体故障重构信号包络谱
Figure 21. Bearing roller fault reconstruction signal envelope spectrum
图 22 WPD-KVI-Hilbert滚动体重构包络谱
Figure 22. WPD-KVI-Hilbert roller reconstruction envelope spectrum
图 24 WPD-KVI-Hilbert外圈重构包络谱
Figure 24. WPD-KVI-Hilbert outer ring reconstruction envelope spectrum
图 25 某型航空发动机主轴承剥落故障试验台
Figure 25. Test bench for main bearing spalling failure of a certain type of aircraft engine
图 26 部件试验器主轴承振动采集系统示意图
Figure 26. Schematic diagram of vibration acquisition system for main bearing of component tester
图 27 31 mm×20 mm外圈真实磨损故障
Figure 27. Outer ring with genuine wear and tear measuring31 mm×20 mm
图 29 轴承外圈故障重构信号包络谱
Figure 29. Bearing outer ring fault reconstruction signal envelope spectrum
图 30 WPD-KVI-Hilbert主轴承外圈重构包络谱
Figure 30. WPD-KVI-Hilbert main bearing outer ring reconstruction envelope spectrum
表 1 试验轴承参数
Table 1. Test bearing parameters
参数 数值 滚动体直径/mm 8 外圈直径/mm 140 内圈直径/mm 110 接触角/（°） 0 滚珠体个数/个 34 
下载: 导出CSV
表 2 各node分量的指标值
Table 2. Index value of each node component
Node K $ r $ $ \varepsilon $ $ {K_{{{r\varepsilon}}}} $ 1 3.50 0.60 0.36 0.01 2 4.29 0.47 0.22 0.04 3 6.75 0.25 0.06 0.12 4 4.10 0.18 0.03 0.02 5 5.32 0.24 0.06 0.07 6 5.21 0.30 0.09 0.06 7 5.84 0.30 0.09 0.09 8 4.09 0.30 0.09 0.03
下载: 导出CSV
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