相关系数包络谱联合指标C<sub>e</sub>K的轴承故障诊断方法

张龙; 赵丽娟; 王朝兵; 刘杨远; 蔡秉桓; 吴荣真

doi:10.13433/j.cnki.1003-8728.20220163

Bearing Fault Diagnosis by Correlation Coefficient Envelope Spectrum Combined with Indicator C_eK

1.
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
2.
CRRC QISHUYAN Co., Ltd., Changzhou 213011, Jiangsu, China

摘要

摘要: 针对轴承故障冲击特征提取时存在噪声和转频等成分的干扰问题，提出一种相关系数包络谱联合综合指标C_eK的轴承故障诊断方法。在信号预处理阶段采用新的综合指标C_eK从总体平均经验模态分解(Ensemble empirical mode decomposition，EEMD)得到的多个本征模态分量(Intrinsic mode function，IMF)中选取最优分量，进一步使用最大相关峭度反褶积(Maximum correlated kurtosis deconvolution，MCKD)对最优分量进行滤波降噪处理，最后使用Laplace小波相关滤波法提取故障冲击相关系数的峰值，做相关系数的包络谱图。通过仿真信号的分析结果，验证了本文方法的可行性。借助于南昌铁路局采集的真实故障信号，并以峭度指标代替本文提出的综合指标进行后续处理以及自适应Morlet小波滤波提取故障特征的分析结果，突出了利用综合指标C_eK和相关系数包络谱提取故障特征频率的优越性。
- 滚动轴承 /
- 综合指标C_eK /
- Laplace小波 /
- 相关系数包络谱
Abstract: Seeking to the interference problem of components such as noise and rotating frequency when extracting the impact characteristics of bearing faults, a bearing fault diagnosis method by correlation coefficient envelope spectrum combined with indicator C_eK is proposed. In the signal preprocessing stage, EEMD is utilized to decompose the original vibration signal into multiple intrinsic modal components (IMFs), and the optimal modal component is selected based on the principle of the proposed new comprehensive index C_eK value, the MCKD is further used to filter and reduce noise on the optimal component. Finally, the peak value of the correlation coefficient is extracted by the Laplace wavelet correlation filtering method, and the envelope spectrum of the correlation coefficient is made. The feasibility of the proposed approach is verified by the analysis results of the simulated signals. With the help of the real fault signals collected by Nanchang Railway Bureau, and replacing the proposed comprehensive index with kurtosis for subsequent processing and the analysis results of adaptive Morlet wavelet filtering to extract fault features, the superiority of this study in extracting fault feature frequency using the comprehensive index C_eK and correlation coefficient envelope spectrum is verified.
- rolling bearing /
- comprehensive index C_eK /
- laplace wavelet /
- spectrum of correlation coefficie

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图 1 Laplace小波波形和频谱图

Figure 1. The waveforms and frequency spectras of Laplace wavelet

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图 2 本文所提方法流程图

Figure 2. The flowchart of the proposed method

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图 3 外圈仿真信号及其EEMD分解图

Figure 3. The simulated signals of the outer ring and their EEMD decompositions

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图 4 本文方法仿真信号分析结果

Figure 4. The simulation signal analysis results

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图 5 JL-501机车轴承检测台

Figure 5. Inspection bench for JL-501 locomotive bearings

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图 6 轴承内、外圈故障图

Figure 6. A bearing's inner and outer ring faults

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图 7 内圈信号分析

Figure 7. Outer ring's signals analysis

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图 8 本文方法内圈分析结果

Figure 8. Analysis results of the inner ring

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图 9 峭度指标内圈分析结果

Figure 9. Kurtosis analysis results of the inner ring

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图 10 对比方法内圈分析结果

Figure 10. Comparsion of inner ring analysis results

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图 11 本文方法外圈分析结果

Figure 11. Outer ring analysis results using the proposed method

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图 12 峭度指标外圈分析结果

Figure 12. Kurtosis results of the outer ring

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图 13 对比方法外圈分析结果

Figure 13. Comparsion of outer ring analysis results

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表 1 仿真信号各分量对应的指标值

Table 1. Indicator values for simulation signal components

名称	IMF₁	IMF₂	IMF₃	IMF₄	IMF₅
C_e	15.40	13.45	12.17	11.24	8.55
CK	1.54	1.45	5.27	2.16	1.47
C_eK	9.99	9.26	2.31	5.20	5.82

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表 2 传感器主要性能参数

Table 2. Main performance parameters of the sensor

性能	参数值
灵敏度（2 ± 5℃）	100 mV/g
测量范围（峰值）	± 50 g
频率响应（ ± 5%）	1 ~ 5000 Hz
工作温度范围	−40 ~ + 120 ℃
壳体材料	304 不锈钢
噪声	<30 μV
幅值线性度	≤2%

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表 3 内圈信号各分量对应的指标值

Table 3. Indicator values for the inner ring signal components

名称	IMF₁	IMF₂	IMF₃	IMF₄	IMF₅
C_e	14.48	16.14	12.50	10.57	12.11
CK	4.34	1.71	3.67	2.49	3.40
C_eK	3.33	9.46	3.41	4.24	3.57

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表 4 外圈信号各分量对应的指标值

Table 4. Indicator values for the outer ring signal components

名称	IMF₁	IMF₂	IMF₃	IMF₄	IMF₅
C_e	14.17	14.62	13.96	13.69	12.44
CK	2.07	5.78	1.54	4.16	2.02
C_eK	6.85	2.53	9.05	3.29	6.15

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