Rolling bearing vibration feature extraction and characterization method based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization algorithm
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摘要:
针对滚动轴承早期微弱故障受背景环境噪声影响故障特征难以提取的问题，提出一种基于灰狼算法（GWO）优化最大相关峭度反卷积的滚动轴承振动特征提取与表征方法。该方法采用完全自适应噪声集合经验模态分解（CEEMDAN）将受强背景环境噪声干扰的微弱故障振动信号分解成若干信号分量，并依据峭度指标和相关系数作为筛选指标对各信号分量进行筛选和重构，通过GWO优化的最大相关峭度反卷积（MCKD）滤除重构信号中的噪声成分同时增强微弱故障特征成分，并对其进行包络解调实现微弱故障特征的提取。基于滚动轴承实验台数据及真实涡扇发动机整机数据开展了滚动轴承故障特征提取与表征方法有效性的综合验证。结果表明：该方法可有效滤除滚动轴承微弱故障振动信号中的强背景环境噪声成分同时增强微弱故障特征，经主轴承外圈微弱故障实验数据验证可知去噪信号与原始信号的峰值因子相比提高了2.43，有效增强振动信号中的冲击性成分，实现滚动轴承微弱故障特征的有效提取与表征。
Abstract:In view of the problem that the weak fault characteristics of rolling bearings in the early stage affected by background environmental noise are difficult to be extracted, a rolling bearing vibration feature extraction and characterization method was proposed based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization (GWO) algorithm. The complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) was used to decompose the weak fault vibration signal disturbed by strong background environmental noise into several signal components, and the signal components were screened and reconstructed according to the kurtosis and correlation coefficient as the screening index in this method. The maximum correlated kurtosis deconvolution (MCKD) optimized by the GWO algorithm filtered out the noise components in the reconstructed signal, enhanced the weak fault feature components and performed envelope demodulation to extract the weak fault features. A comprehensive verification of the effectiveness of the vibration signal fault feature extraction and characterization method was carried out based on the rolling bearing test bench data and the real whole machine data of the aero-engine. The results showed that this method can effectively filter out the strong background environmental noise part in the weak fault vibration signal and enhance the weak fault characteristics, indicating that the peak factor of the denoising signal processed by this method increased by 2.43 compared with the original vibration signal in the turbofan engine experiment, so it effectively enhanced the shock component in the vibration signal. The method proposed can be used as one of the effective methods for fault diagnosis of aero-engine.
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图 3 基于GWO优化最大相关峭度反卷积的滚动轴承振动特征提取与表征方法
Figure 3. Rolling bearing vibration feature extraction and characterization method based on maximum correlated kurtosis deconvolution optimized by gray wolf optimization algorithm
图 6 外圈故障白噪声仿真信号基于本文方法处理的噪声滤除结果
Figure 6. Noise filtering result of outer ring fault with white noise simulated signal based on method proposed
图 7 外圈故障白噪声仿真信号去噪结果0～600 Hz包络谱
Figure 7. 0—600 Hz envelope spectrum of denoising result of outer ring fault with white noise simulation signal
图 8 基于本文提出方法分析的轴承外圈故障强背景噪声仿真信号包络谱
Figure 8. Envelope spectrum of outer ring fault with strong background noise base on method proposed
图 9 基于PSO-MCKD-HHT方法分析的轴承外圈故障强背景噪声仿真信号包络谱
Figure 9. Envelope spectrum of outer ring fault with strong background noise base on PSO-MCKD-HHT
图 10 西储大学滚动轴承实验台
Figure 10. Rolling bearing data acquisition test bench of Western Reserve University
图 12 内圈故障样本的标准指标与对比指标
Figure 12. Standard parameter and comparison parameters of inner ring fault sample
图 13 内圈故障样本去噪信号与原始信号对比
Figure 13. Inner ring fault sample denoising signal compared to the original signal
图 14 内圈故障样本去噪信号包络谱
Figure 14. Envelope spectrum of inner ring fault sample denoising signal
图 15 内圈故障样本原始信号包络谱
Figure 15. Envelope spectrum of inner ring fault sample original signal
图 16 外圈故障样本去噪信号包络谱
Figure 16. Envelope spectrum of outer ring fault sample denoising signal
图 17 外圈故障样本原始信号包络谱
Figure 17. Envelope spectrum of outer ring fault samples original signal
图 18 涡扇发动机整机实验台内部结构简图及振动信号传递路径
Figure 18. Schematic diagram of the internal structure of the turbofan engine test bench and vibration signal transmission path
图 20 整机故障样本原始振动信号
Figure 20. Original vibration signal of the whole machine fault sample
图 21 整机故障样本去噪信号和原始信号对比
Figure 21. Whole machine fault sample denoising signal compared to the original signal
图 23 基于PSO-MCKD-HHT处理M2外部测点去噪信号包络谱
Figure 23. Envelope spectrum of M2 external measurement point denoising signal based on the PSO-MCKD-HHT method
表 1 6205-2RS JEM SKEM深沟球轴承参数
Table 1. 6205-2RS JEM SKEM deep groove ball bearing parameters
参数 数值 滚道节径/mm 39.0390 滚动体直径/mm 7.940 接触角/（°） 0 滚动体个数 9 
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表 2 内、外圈故障实验样本参数
Table 2. Inner and outer ring fault experimental sample parameters
参数 数值 转速/（r/min） 1797 故障直径/mm 0.1778 故障深度/mm 0.2794 电动机功率/kW 0 采样频率/kHz 12 特征频率/Hz 内圈故障 162.2 外圈故障 107.4
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表 4 “6222S型”振动信号传感器的相关参数
Table 4. related parameters of “6222S” acceleration vibration sensor
参数 数值 响应频率/kHz 28 温度范围/℃ −54～260 幅值线性度/% 1/250 g
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