Rolling Bearing Fault Diagnosis Research Combined Parameter Optimization VMD with OMPE
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摘要: 针对难以判别的轴承运行振动信号中的状态特征, 提出参数优化变分模态分解(VMD)和最优多尺度排列熵(OMPE)结合的特征向量构建的方法, 采用支持向量机(SVM)进行故障诊断。VMD的分解结果由分解个数和惩罚因子限制, 采用量子粒子群算法(QPSO)优化达到分解的最优效果。考虑轴承运行的周期性质, 提出基于轴承故障运行周期特性的最优多尺度排列熵概念, 运用模态分量与最优尺度排列熵结合构建特征向量。通过不同方法采用支持向量机识别对比分析, 表明上述提出的方法能有效提取特征, 提高轴承的故障诊断的精度。Abstract: Aiming at the state features in the vibration signal of rolling bearing operation which are difficult to distinguish, a method of constructing feature vectors combined with parameter optimization variational modal decomposition (VMD) and optimal multi-scale permutation entropy (OMPE) is proposed, and support vector machine (SVM) is next used to diagnose faults. The decomposition result of VMD is limited by the number of decompositions and the penalty factor, and the quantum particle swarm optimization (QPSO) is used to achieve the optimal effect of decomposition. Considering the periodic nature of bearing operation, the concept of optimal multi-scale permutation entropy based on the period characteristics of bearing fault operation is proposed, and the feature vector is constructed by combining modal components and optimal-scale permutation entropy. Using support vector machine identification and comparative analysis through different methods shows that the proposed method can effectively extract features and improve the accuracy of rolling bearing fault diagnosis.
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表 1 不同K值下所对应的中心频率
K 中心频率/Hz 3 525 2 113 2 981 - - - - 4 507 1 134 2 245 2 984 - - - 5 505 1 130 2 137 2 818 3 037 - - 6 503 1 125 2 075 2 365 2 847 3 042 - 7 492 1 002 1 199 2 185 2 760 2 975 3 114 
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表 2 不同α值下对应的中心频率
α 中心频率/Hz 500 504 1 130 2 171 2 752 2 974 3 114 1 000 504 1 130 2 171 2 752 2 974 3 113 1 500 504 1 130 2 171 2 751 2 974 3 113 2 000 503 1 125 2 075 2 365 2 847 3 042 2 500 503 1 125 2 075 2 365 2 847 3 042 3 000 503 1 125 2 075 2 365 2 847 3 042
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表 3 参数组合在[6, 2 000]时的包络熵值
n 1 2 3 4 5 6 Ep 6.797 6 6.521 9 6.651 7 6.517 2 6.445 0 6.570 4
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表 4 采用VMD-PE方法构建的特征向量
标签 特征值1 特征值2 特征值3 特征值4 1 1.108 9 1.372 2 1.673 2 1.713 2 2 1.213 3 1.357 2 1.499 6 1.619 0 3 1.110 8 1.344 6 1.580 2 1.644 3 4 0.930 2 1.277 5 1.583 5 1.716 8
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