Rolling bearing fault diagnosis method based on GWO-NLM and CEEMDAN
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摘要:
针对滚动轴承故障振动信号受背景噪声干扰大、故障特征不易提取的问题,提出了基于灰狼算法(GWO)优化的非局部均值去噪(NLM)和完全自适应噪声集合经验模态分解(CEEMDAN)相结合的轴承故障诊断方法。先将CEEMDAN和相关系数-能量比-峭度准则作为预处理手段,并进行信号重构;然后使用灰狼算法对NLM的参数进行优化,利用最优参数对重构信号进行降噪,将降噪后的信号通过SG(Savitzky-Golay)滤波进行二次降噪,得到最终去噪信号,对最终信号进行包络分析得到诊断结果。GWO-NLM去噪、CEEMDAN和包络分析的混合特征提取技术,由仿真信号可知去噪后的信噪比提高了9.31 dB,由实验信号可知能清晰地提取轴承的故障特征频率及倍频、转频以及故障特征频率与转频的系列调制频率。
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关键词:
- 完全自适应噪声集合经验模态分解(CEEMDAN) /
- 非局部均值去噪(NLM) /
- 包络谱分析 /
- 灰狼算法 /
- 特征提取 /
- 故障诊断
Abstract:For the problem that the vibration signal of rolling bearing faults is disturbed by background noise and the fault features are not easily extracted, a combination of non-local mean denoising (NLM) based on the optimization of the gray wolf algorithm (GWO) and fully adaptive noise-enabled ensemble empirical modal decomposition (CEEMDAN) was proposed for bearing fault diagnosis. First, CEEMDAN and the Correlation coefficient-energy ratio-kurtosis criterion were used as preprocessing way, and signal reconstruction was performed; then the grey wolf algorithm was used to optimize the parameters of NLM, and the optimal parameters were used to denoise the reconstructed signal, and secondary denoising of the denoised signal was achieved through SG (Savitzky-Golay) filtering to obtain the final denoised signal, and envelope analysis of the final signal was performed to obtain diagnostic results. For the hybrid feature extraction technique of GWO-NLM denoising, CEEMDAN and envelope analysis, the signal-to-noise ratio was improved by 9.31 dB after denoising as shown by the simulated signal, and the fault characteristic frequency and multiplication frequency of the bearing and the series modulation frequency of the fault characteristic frequency and rotation frequency can be clearly extracted by the experimental signal.
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表 1 不同信号信噪比
Table 1. Signal-to-noise ratio of different signals
信号 信噪比/dB 原始信号 −5.47 NLM 0.638 GWO-NLM 3.51 GWO-NLM-SG 3.84 表 2 6205-2RS JEM SKF深沟球轴承几何参数
Table 2. 6205-2RS JEM SKF deep groove ball bearing geometric parameters
参数 数值 节径 D/mm 39.039 滚珠直径 d/mm 7.940 接触角 $ \alpha $/(°) 0 滚珠数 Z 9 表 3 内圈故障数据实验参数
Table 3. Experimental parameters for inner ring fault data
参数 数值 转速/(r/min) 1797 故障直径/mm 0.18 故障深度/mm 0.28 负载/W 0 表 4 外圈故障数据实验参数
Table 4. Experimental parameters for outer ring fault data
参数 数值 转速/(r/min) 1797 故障直径/mm 0.18 故障深度/mm 0.28 表 5 前6个IMFs的能量比、相关系数、峭度
Table 5. Energy ratio, correlation coefficient, kurtosis of the first six IMFs
IMF 能量比 相关系数 峭度 $K_ { {\rm{r} }\text{ε} }$值 1 1.00 0.86 0.15 0.67 2 0.02 0.44 1.00 0.48 3 0.07 0.45 0.25 0.25 4 0.05 0.35 0.03 0.14 5 0.06 0.23 0.02 0.106 6 0.05 0.10 0.01 0.04 表 6 转频及其倍频、故障频率及其倍频、调制频率(部分包络谱、外圈故障)
Table 6. Rotation frequency and its multiplier, fault frequency and its multiplier, modulation frequency (partial envelope spectrum, outer ring fault)
特征频率 n 1 2 3 4 5 6 7 $n{F_{\text{r}}}$ √ √ √ √ √ $n{f_{\rm{o}}}$ √ √ √ ${f_{\rm{o}}} - n{F_{\rm{r}}}$ √ √ √ ${f_{\rm{o}}} + n{F_{\rm{r}}}$ √ √ √ √ √ $2{f_{\rm{o}}} - n{F_{\rm{r}}}$ √ √ √ √ √ $2{f_{\rm{o}}} + n{F_{\rm{r}}}$ √ √ √ √ $3{f_{\rm{o}}} - n{F_{\rm{r}}}$ √ √ √ √ √ √ √ $3{f_{\rm{o}}} + n{F_{\rm{r}}}$ √ √ 注:n为频率倍数;√代表可以提取出该特征频率。 表 7 NJ204EM滚棒轴承参数
Table 7. Parameters of NJ204EM roller bearing
参数 数值 节径D/mm 33.5 滚棒直径d/mm 7 接触 α/(°) 0 滚棒数Z 11 -
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