Fault diagnosis of rolling bearing based on wavelet threshold noise reduction EMD-AR spectrum analysis and extreme learning machine
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摘要: 针对传统的滚动轴承故障诊断中，振动特征易受冗余噪声干扰，且不能对故障特征准确分类的问题，提出1种基于小波降噪、EMD-AR谱分析和ELM（极限学习机）的滚动轴承故障诊断的方法。对滚动轴承振动信号首先进行小波阈值降噪处理，随后将降噪后的一维信号进行EMD分解并提取其前6个IMF分量，将前6个IMF分量的AR谱累加得到降噪后振动信号的EMD-AR谱，可从谱中看出轴承不同的故障情况来作为先验诊断。最后提取降噪后信号的6个特征值作为样本，为避免实验的偶然性，建立基于K折交叉验证ELM分类诊断模型。诊断结果表明，该方法能对轴承故障情况进行清楚分类，分类精度最高可达100%，可对轴承故障诊断提供新的方法。Abstract: In the traditional rolling bearing fault diagnosis, vibration characteristics are easy to be interfered by redundant noise, and fault characteristics can not be accurately classified, a rolling bearing fault diagnosis method based on wavelet denoising, EMD-AR spectrum analysis and ELM (extreme learning Machine) was proposed. The vibration signals of rolling bearings are firstly de-noised by wavelet threshold, then the de-noised one-dimensional signals are decomposed by EMD and the first six IMF components are extracted. The AR spectra of the first six IMF components are accumulated to obtain the EMD-AR spectra of the de-noised vibration signals, from which different fault conditions of bearings can be seen as a prior diagnosis. Finally, six eigenvalues of the denoised signal were extracted as samples. In order to avoid the contingency of the experiment, the ELM classification diagnosis model based on K-fold cross-validation was established. The diagnosis results show that the method can clearly classify the bearing fault situation, the classification accuracy is up to 100%, and can provide a new method for the bearing fault diagnosis.
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Key words:
- rolling bearing /
- fault diagnosis /
- threshold noise reduction /
- EMD-AR spectrum /
- ELM
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表 2 正常信号的特征值
工况 平均值 方差 最大值 最小值 峭度指标 波形因子
正常0.014 70 0.001 33 0.115 70 −0.082 25 3.459 07 1.307 16 0.014 14 0.001 75 0.125 12 −0.086 52 3.001 30 1.222 37 0.015 09 0.001 13 0.086 55 −0.055 00 2.457 99 1.164 05 0.010 37 0.001 59 0.102 04 −0.061 62 2.261 75 1.196 22 
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表 3 内套故障信号的特征值
工况 平均值 方差 最大值 最小值 峭度指标 波形因子 内套故障 0.002 97 0.007 91 0.521 42 −0.420 63 10.912 59 1.431 36 0.004 79 0.004 91 0.307 21 −0.327 43 7.378 57 1.382 04 0.005 76 0.006 12 0.388 34 −0.309 16 6.898 75 1.350 04 0.004 13 0.005 82 0.249 59 −0.292 07 3.789 02 1.281 29
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表 4 滚动体故障信号的特征值
工况 平均值 方差 最大值 最小值 峭度指标 波形因子 滚动体
故障0.004 95 0.000 87 0.064 06 −0.054 60 2.093 01 1.182 57 0.005 36 0.001 08 0.107 09 −0.067 63 3.787 17 1.322 65 0.004 38 0.000 56 0.062 09 −0.044 65 2.919 65 1.230 67 0.005 22 0.000 65 0.069 73 −0.064 12 3.291 83 1.276 55
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表 5 外圈故障信号的特征值
工况 平均值 方差 最大值 最小值 峭度指标 波形因子 外圈故障 0.003 86 0.027 43 0.750 23 −0.871 38 13.968 01 1.982 72 0.004 58 0.036 59 0.951 89 −1.197 16 19.221 75 2.159 15 0.003 93 0.026 56 1.260 57 −0.994 98 28.511 19 2.536 65 0.004 33 0.043 52 1.091 19 −1.202 59 16.612 26 2.099 04
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