Axle-Box Bearing Fault Diagnosis of Railway Vehicle Based on Enhanced Time-Varying Morphological Filtering
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摘要:
形态学滤波是一种用于轴承故障诊断的有效方法，能够从嘈杂的振动信号中恢复瞬态脉冲特征，其中结构元素形状和长度的选择对形态学滤波性能有着至关重要的影响. 为解决这个问题，提出一种基于中值滤波的增强时变结构元素，以更准确地匹配和提取隐藏在嘈杂信号中的周期性瞬态特征；此外，将功率谱（即自相关信号的频谱）应用于滤波信号，以进一步增强故障相关分量并消除宽带噪声污染；最后，提出一种结合增强时变结构元素和功率谱的轴承故障诊断方法——增强时变形态学滤波. 对仿真数据和2个铁路轴箱轴承试验台测量数据的分析结果表明：相较于对比方法，增强时变形态学滤波具有优异的故障特征提取性能，能够在复杂噪声干扰下准确识别轴承的内圈、外圈和滚动体故障，并取得更高的性能量化指标和更少的计算消耗.
Abstract:Morphological filtering (MF) is an effective method for bearing fault diagnosis with the capacity of recovering transient impulse features from noisy vibration signals, in which the choice of shape and length of structural element has an important impact on MF performance. To solve this problem, an enhanced time-varying structural element (ETVSE) based on median filtering was proposed to more accurately match and extract periodic transient features hidden in noisy signals. Moreover, the power spectrum (i.e., the frequency spectrum of autocorrelation signal) was applied to the filtered signal to further enhance fault-related components and eliminate broadband noise pollution. Finally, a bearing fault diagnosis method called enhanced time-varying morphological filtering (ETVMF) was developed, which combined the advantages of ETVSE and power spectrum. The analysis results of simulated data and measured data of two railway axle-box bearing test rigs show that, compared with the compared method, ETVMF demonstrates superior fault feature extraction performance and can accurately identify bearing inner race, outer race, and rolling element faults under complex noise interference, while obtaining higher performance quantification index and lower calculation cost.
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图 3 轴承内圈故障仿真信号及其包络谱
Figure 3. Simulation signal of bearing inner race fault and its envelope spectrum
图 6 不同方法分析具有不同信号分量强度的仿真信号时得到的CFIC
Figure 6. Characteristic frequency intensity coefficients （CFICs） obtained by analyzing simulation signals with different signal component strengths by different methods
图 8 滚动体故障轴箱轴承的振动信号及其包络谱
Figure 8. Vibration signal of axle-box bearing with rolling element fault and its envelope spectrum
图 9 ETVMF方法的滚动体故障检测结果
Figure 9. Rolling element fault detection results of ETVMF method
图 10 ATVMF-DSS方法的滚动体故障检测结果
Figure 10. Rolling element fault detection results of ATVMF-DSS method
图 12 外圈故障轴箱轴承的振动信号及其包络谱
Figure 12. Vibration signal of axle-box bearing with outer race fault and its envelope spectrum
表 1 基本形态学算子
Table 1. Basic morphological operators
算子 定义 膨胀 $ (f \oplus g)(n) = \mathop {\max }\limits_{0 \leqslant m \leqslant M - 1} [f(n - m) + g(m)] $ 腐蚀 $ (f\Theta g)(n) = \mathop {\min }\limits_{0 \leqslant m \leqslant M - 1} [f(n + m) - g(m)] $ 开 $ (f \circ g)(n) = (f\Theta g \oplus g)(n) $ 闭 $ (f \cdot g)(n)=(f\oplus g\Theta g)(n) $ 开-闭 $ {F}_{\text{OC}}(n)=(f\circ g \cdot g)(n) $ 闭-开 $ {F}_{\text{CO}}(n)=(f \cdot g\circ g)(n) $ 
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表 2 不同方法的CFIC比较
Table 2. Comparison of CFICs of different methods
故障类型 ETVMF ATVMF-DSS MSMHPF MMF 内圈故障 5.776 2.429 3.116 2.455 滚动体故障 4.425 2.297 1.937 1.929 外圈故障 6.551 1.544 4.318 2.148
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表 3 不同方法的Hoyer测度比较
Table 3. Comparison of Hoyer measure of different methods
故障类型 ETVMF ATVMF-DSS MSMHPF MMF 内圈故障 0.977 0.620 0.663 0.689 滚动体故障 0.875 0.763 0.762 0.723 外圈故障 0.971 0.662 0.864 0.773
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表 4 不同方法的频域信噪比比较
Table 4. Comparison of frequency-domain signal-to-noise ratios of different methods
故障类型 ETVMF ATVMF-DSS MSMHPF MMF 内圈故障 1.458 0.549 0.511 0.335 滚动体故障 0.952 0.667 0.564 0.354 外圈故障 2.049 0.148 0.779 0.402
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表 5 不同方法的计算效率比较
Table 5. Comparison of computational efficiencies of different methods
s 故障类型 ETVMF ATVMF-DSS MSMHPF MMF 内圈故障 0.363 1.978 269.142 0.012 滚动体故障 0.257 0.730 98.700 0.011 外圈故障 0.218 0.795 162.544 0.008
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