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Fault Diagnosis of Spindle Device in Hoist Using Variational Mode Decomposition and Statistical Features
Shock and Vibration ( IF 1.6 ) Pub Date : 2020-09-23 , DOI: 10.1155/2020/8849513
Jun Gu 1 , Yuxing Peng 1, 2 , Hao Lu 1, 2 , Shuang Cao 1 , Bobo Cao 1
Affiliation  

By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist, it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system. In response to this problem, this paper proposes a new approach based on variational mode decomposition (VMD), SVM, and statistical characteristics such as variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Comparisons have gone to evaluate the performance of rolling bearing defect by using EMD (Empirical Mode Decomposition), MEEMD (Modified Ensemble EMD), BP (Back Propagation) network, single or multiple statistical characteristics, and different motor loads. The experiment was carried out on the mechanical failure simulator of the main shaft device of the hoist, which verified the reliability and effectiveness of the method. The results show that the diagnosis method is suitable for feature extraction of bearing fault signals, with the highest diagnosis accuracy. It can provide a good practical reference for the fault diagnosis of mechanical equipment of the hoist spindle device and has certain practical value.

中文翻译:

基于变分分解和统计特征的提升机主轴装置故障诊断

通过分析来自矿井提升机主轴装置的非线性和非平稳振动信号,克服故障特征提取的困难并准确识别转子轴承系统的故障是一个挑战。针对此问题,本文提出了一种基于变分模式分解(VMD),SVM和方差贡献率(VCR),能量熵(EE)和置换熵(PE)等统计特征的新方法。通过使用EMD(经验模态分解),MEEMD(改进的集成EMD),BP(反向传播)网络,单个或多个统计特性以及不同的电动机负载,进行了比较以评估滚动轴承缺陷的性能。实验是在提升机主轴装置的机械故障模拟器上进行的,验证了该方法的可靠性和有效性。结果表明,该诊断方法适用于轴承故障信号的特征提取,诊断准确率最高。可以为提升主轴装置机械设备的故障诊断提供良好的实用参考,具有一定的实用价值。
更新日期:2020-09-23
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