基于VMD-RQA的直线振动筛激振力不平衡故障诊断

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (18) : 25-32.

论文

范伟1,2，何越宙1，王寅1，陈华1

作者信息 +

Early unbalance fault diagnosis on the exciting force of a linear vibrating screen based on VMD-RQA

FAN Wei1,2，HE Yuezhou1，WANG Yin1，CHEN Hua1

Author information +

文章历史 +

摘要

针对直线振动筛早期激振力不平衡故障难以诊断问题，提出了一种基于变分模态分解(VMD)和递归量化分析(RQA)的故障诊断方法。对振动信号进行VMD分解，将直线振动筛基频信号分离，并得到被淹没的各阶高频分量；绘制不同信号分量的动力特性递归图，计算递归图的量化指标，组成故障信号的非线性、非平稳性评价特征向量，将高维特征向量输入机器学习分类器中进行识别诊断，并与传统的特征提取方法比较。试验结果表明：在直线振动筛激振力不平衡故障现场，该方法所提取的特征参数具有最高识别精度，综合识别率为99.13%；且应用于旋转机械滚动轴承实例数据，综合识别率为99.38%，说明该方法具有一定的通用性和工程应用价值。

Abstract

Aiming at the difficulty to diagnose the early unbalance fault on the exciting force of a linear vibrating screen, a fault diagnosis method based on variational mode decomposition(VMD) and recurrence quantitative analysis(RQA) was proposed.Firstly, the vibration signal was decomposed by VMD, the fundamental frequency signal of the linear vibrating screen was separated, and the submerged high frequency components were obtained.Then, the dynamic characteristic recurrence plots of different signal components were drawn, and the quantitative indexes of the recurrence plots were calculated to constitute a nonlinear and nonstationary evaluation feature vector of the fault signal.Finally, the high-dimensional eigenvectors were input into a machine learning classifier for diagnosis, and its results were compared with those of traditional feature extraction methods.The results show that, the characteristic parameters extracted by the method has the highest recognition accuracy in the excitation force unbalance fault experiment, and the comprehensive recognition rate is 99.13%.In addition, applying the method to the fault diagnosis of rotating machinery bearings, the comprehensive recognition rate reaches 99.38%, indicating that the method has certain reliability and engineering application value.

导出引用

范伟，何越宙，王寅，陈华. 基于VMD-RQA的直线振动筛激振力不平衡故障诊断[J]. 振动与冲击, 2021, 40(18): 25-32

FAN Wei，HE Yuezhou，WANG Yin，CHEN Hua. Early unbalance fault diagnosis on the exciting force of a linear vibrating screen based on VMD-RQA[J]. Journal of Vibration and Shock, 2021, 40(18): 25-32

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