Axle-Box Bearing Fault Diagnosis Based on Multiband Weighted Envelope Spectrum
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摘要:
为增强复杂噪声干扰下轴箱轴承故障检测的鲁棒性，基于循环谱分析并考虑轴承故障信息分布差异和阈值降噪，对轴箱轴承故障诊断的包络谱构造方法进行了研究. 首先，提出频域信噪比作为轴承故障信息量化的新测度，用于评估谱相干中不同谱频带内的轴承故障相关信息；其次，构造以谱频率为变量的故障特征信息分布函数，并自适应确定信息阈值来辨识谱相干中故障信息丰富和干扰噪声主导的谱频率分量，进一步基于故障特征信息分布函数和信息阈值设计权重函数；最后，由谱相干和权重函数生成融合多带信息的多带加权包络谱，通过分析谱中的轴承故障特征频率来检测轴箱轴承的不同故障. 铁路轴箱轴承实验数据的分析结果表明：相比于基于谱相干的典型包络谱方法，多带加权包络谱能够在复杂噪声干扰下准确识别轴箱轴承的外圈、滚动体和内圈故障，并能取得更高的性能量化指标（频域信噪比和负熵）.
Abstract:To enhance the robustness of axle-box bearing fault detection under complex interference noise, the envelope spectrum construction method for axle-box bearing fault diagnosis is investigated by cyclic spectral analysis and considering the distribution difference of bearing fault information and threshold denoising. Firstly, the frequency domain signal-to-noise ratio is proposed as a new measure for bearing fault information quantification to evaluate the fault-related information in different spectral frequency bands of the spectral coherence. Secondly, a fault characteristic information distribution function with spectral frequency as the variable is constructed and an information threshold is adaptively determined to identify the spectral frequency components that are rich in fault information and dominated by interference noise in the spectral coherence; further, a weight function based on the fault characteristic information distribution function and the information threshold is designed. Finally, a multiband weighted envelope spectrum is generated from the spectral coherence with the weight function and is used to detect different axle-box bearing faults by analyzing the bearing fault characteristic frequencies. The analysis results of the experimental data of railway axle-box bearings show that compared with typical spectral coherence-based envelope spectrum methods, the multiband weighted envelope spectrum can accurately detect the faults of the outer race, rolling element and inner race of the axle-box bearing under complex interference noise and can achieve higher performance quantification indicators (frequency domain signal-to-noise ratio and negentropy).
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图 2 外圈故障轴箱轴承的振动信号及其谱相干和EES
Figure 2. Vibration signal of axle-box bearing with outer race fault and its spectral coherence and EES
图 3 外圈故障轴箱轴承的振动信号的Hilbert包络谱
Figure 3. Hilbert envelope spectrum of the vibration signal of axle-box bearing with outer race fault
图 7 滚动体故障轴箱轴承的振动信号及其谱相干和EES
Figure 7. Vibration signal of axle-box bearing with rolling element fault and its spectral coherence and EES
图 8 滚动体故障轴箱轴承的振动信号的Hilbert包络谱
Figure 8. Hilbert envelope spectrum of the vibration signal of axle-box bearing with rolling element fault
图 9 MWES方法的滚动体故障检测结果
Figure 9. Rolling element fault detection results of the MWES method
图 10 CIES方法的滚动体故障检测结果
Figure 10. Rolling element fault detection results of the CIES method
图 11 WES方法的滚动体故障检测结果
Figure 11. Rolling element fault detection results of the WES method
图 12 内圈故障轴箱轴承的振动信号及其谱相干和EES
Figure 12. Vibration signal of axle-box bearing with inner race fault and its spectral coherence and EES
图 13 内圈故障轴箱轴承的振动信号的Hilbert包络谱
Figure 13. Hilbert envelope spectrum of the vibration signal of axle-box bearing with inner race fault
表 1 不同轴承实验信号包络谱的FDSNR
Table 1. FDSNR of envelope spectra of different bearing experimental signals
故障类型 EES MWES CIES WES 外圈故障 1.912 4.775 3.107 2.384 滚动体故障 1.966 3.604 2.860 3.059 内圈故障 1.507 2.949 1.515 1.714 
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表 2 不同轴承实验信号包络谱的负熵
Table 2. Negentropy of envelope spectra of different bearing experimental signals
故障类型 EES MWES CIES WES 外圈故障 0.040 0.136 0.068 0.046 滚动体故障 0.059 0.176 0.104 0.116 内圈故障 0.062 0.201 0.064 0.072
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