A Fault Diagnosis Method Combining with Quadratic VMD Screening, MPE and FCM
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摘要: 针对单向阀振动信号含有背景噪声，故障特征提取困难和诊断精度不高的问题，提出了二次变分模态分解（二次VMD）、多尺度排列熵（MPE）和模糊C均值聚类（FCM）相结合的故障诊断方法。首先，通过二次VMD对振动信号进行分解，再使用双阈值法筛选得到有用的本征模态函数（IMF）。其次，提取重构信号中具有敏感特性的MPE特征。最后，将故障特征输入至FCM得到聚类中心，并根据海明贴近度对待识别样本进行分类。通过多组对比实验，结果表明二次VMD筛选能有效去除噪声及虚假成分，MPE具有更好的敏感故障特征表征能力。同时，使用FCM对模糊特征进行聚类能够取得比传统支持向量机（SVM）更好的效果。Abstract: In view of the problem that the vibration signal of check valve contains the background noise, difficulty in extracting fault features and low diagnostic precision, a fault diagnosis method combining quadratic VMD (quadratic variational mode decomposition), MPE (multiscale permutation entropy) and FCM (fuzzy C-means clustering) is proposed. Firstly, the vibration signals are decomposed by the quadratic VMD, and then the useful IMF (intrinsic mode functions) are obtained by using the double threshold method. Secondly, the MPE features with sensitive characteristic are extracted from the reconstructed signals. Finally, the fault features are input into the FCM to get the cluster centers, and the samples to be identified are classified according to the Hamming approach degree. Through a number of comparison experiments, the results show that the quadratic VMD screening method can remove the background noise and false components, and the MPE has a better ability to characterize the sensitive features. At the same time, the use of FCM to classify fuzzy features can achieve better results than traditional SVM (support vector machine).
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表 1 不同模态的中心频率
模态数K 中心频率/Hz 2 575 2 309 - - - - 3 569 2 280 2 982 - - - 4 512 1 087 2 284 2 983 - - 5 512 1 085 2 272 2 789 3 016 - 6 511 1 083 2 129 2 365 2 806 3 018 
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表 2 VMD筛选得到的IMF及最佳模态数
状态 δk ek IMF K′ NOR 0.4522, 0.1698 0.6144, 0.2682 2, 1 2 IRF 0.167, 0.136, 0.131, 0.130 0.228, 0.223, 0.170, 0.130 5, 4, 3, 7 4 ORF 0.256, 0.222, 0.195, 0.172, 0.112 0.297, 0.253, 0.178, 0.163, 0.067 6, 5, 4, 7, 3 5 REF 0.245, 0.173, 0.129, 0.126 0.316, 0.216, 0.199, 0.158 6, 5, 4, 7 4
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表 3 二次VMD筛选得到的IMF
状态 δk ek IMF NOR 0.278 2, 0.117 1 0.863 8, 0.136 2 1, 2 IRF 0.256 0, 0.273 8, 0.136 4 0.400 8, 0.382 0, 0.149 3 4, 3, 2 ORF 0.356 0, 0.332 8, 0.278 1 0.404 7, 0.276 5, 0.316 3 3, 2, 4 REF 0.542 6, 0.176 2 0.714 8, 0.232 7 4, 3
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表 4 两种方法的诊断标准
方法 聚类中心 对应状态 VMD筛选提取MPE 0.962 8, 0.962 1, 0.471 8, 0.786 0 IRF 0.244 7, 0.280 6, 0.675 6, 0.742 1 ORF 0.104 2, 0.066 7, 0.887 0, 0.906 1 REF 0.048 9, 0.569 6, 0.671 7, 0.482 9 NOR 二次VMD筛选提取MPE 0.948 4, 0.971 3, 0.521 2, 0.765 2 IRF 0.882 0, 0.778 1, 0.230 8, 0.186 4 NOR 0.202 9, 0.262 2, 0.755 2, 0.727 2 ORF 0.038 8, 0.079 6, 0.909 4, 0.883 4 REF
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表 5 VMD筛选提取MPE的诊断结果
表 6 二次VMD筛选提取MPE的诊断结果
表 7 二次VMD筛选得到的IMF
状态 IMF δk ek K′ NC 1, 2, 4, 3 0.611, 0.315, 0.158, 0.117 0.319, 0.308, 0.230, 0.089 4 NK 1, 2 0.350 1, 0.275 1 0.906 5, 0.061 0 2 NM 2, 1, 3 0.674 9, 0.306 5, 0.163 7 0.445 5, 0.403 6, 0.140 5 3
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表 8 不同分解方法诊断结果对比
方法 状态 诊断结果 精度 分类系数 模糊熵 NC NK NM 原信号+MPE NC 14 5 1 NK 6 13 1 71.67% 0.642 5 0.633 5 NM 0 4 16 VMD筛选+MPE NC 20 0 0 NK 3 17 0 91.67% 0.850 3 0.365 2 NM 0 2 18 二次VMD筛选+MPE NC 20 0 0 NK 3 17 0 95% 0.881 2 0.259 8 NM 0 0 20
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表 9 不同特征量的诊断结果对比
方法 状态 诊断结果 精度 分类系数 模糊熵 NC NK NM VMD+SE NC 20 0 0 NK 5 15 0 85% 0.702 8 0.529 6 NM 0 4 16 VMD+PE NC 20 0 0 NK 1 19 0 88.33% 0.738 1 0.494 6 NM 0 6 14 VMD+MPE NC 20 0 0 NK 4 16 0 90% 0.806 4 0.391 7 NM 0 2 18
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表 10 不同诊断方法的诊断结果对比
方法 状态 诊断结果 精度/% NC NK NM 二次VMD筛选+MPE+SVM NC 15 5 0 NK 2 18 0 86.667 NM 1 0 19 二次VMD筛选+MPE+FCM NC 20 0 0 NK 3 17 0 95 NM 0 0 20
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