基于改进Hilbert-Huang变换的电动机转子不平衡故障诊断

发布时间：2018-11-03 08:28

【摘要】：在现代工业生产中,电动机作为主要的运行部件,其工作是否正常直接关系到整个工业生产状况的好坏。转子不平衡故障是电动机故障中比较常见的一种类型,其不容易被检测,而且有较大危害性。因此,研究转子不平衡故障诊断技术,以便在早期对电动机转子不平衡故障做出诊断,防止不平衡故障的进一步恶化,具有重要的理论研究意义和实际经济价值。本文首先阐述了电动机转子不平衡故障诊断的发展状况,以及振动分析方法的研究现状,为采用Hilbert-Huang变换方法对转子不平衡故障进行检测和诊断奠定了理论基础。Hilbert-Huang变换方法包含经验模态分解(EMD)和Hilbert谱分析两个过程。EMD能够对非线性和非平稳信号进行很好的分解,以得到期望的本征模态函数(IMF);对IMF进行Hilbert变换,可得信号的频谱图和边际谱图,以此对转子不平衡故障信号进行分析。其次,针对Hilbert-Huang变换方法在对信号进行经验模态分解过程中出现的端点效应问题,在研究传统抑制端点效应方法的基础上,提出了基于镜像延拓和支持向量回归机相结合的端点延拓改进方法。此改进方法先是利用支持向量回归机对原始信号的极值点数据序列两端进行预测,然后用镜像延拓法确定所预测极值点的位置。它解决了支持向量回归机对长数据序列预测不准确,以及镜像延拓法对端点不是极值点的短数据序列处理效果不佳等问题。通过仿真实验对此改进方法和传统的端点延拓方法进行定性对比分析,结果表明,改进的方法能很好地抑制经验模态分解产生的端点效应。然后,将改进的Hilbert-Huang变换方法对电动机转子不平衡故障进行诊断分析,从得到的IMF图和Hilbert边际谱图中可以很精确地提取出不平衡故障信号的特征。最后,结合本文提出的Hilbert-Huang变换改进方法,设计了一套基于LabVIEW的电动机转子不平衡故障诊断系统,通过实验分析,该系统能够有效的对电动机转子不平衡故障进行诊断。
[Abstract]:In modern industrial production, motor as the main operating parts, its normal work directly related to the quality of the whole industrial production. Rotor unbalance fault is a common type of motor fault, which is not easy to detect and has great harm. Therefore, it is of great theoretical significance and practical economic value to study the rotor unbalance fault diagnosis technology in order to diagnose the rotor unbalance fault in the early stage and to prevent the further deterioration of the unbalanced fault. In this paper, the development of rotor unbalance fault diagnosis and the research status of vibration analysis methods are first described in this paper. The method of Hilbert-Huang transform includes two processes: empirical mode decomposition (EMD) and Hilbert spectrum analysis. EMD can deal with nonlinear and non-nonlinear faults. Smooth signals are decomposed very well, To obtain the expected eigenmode function (IMF); By Hilbert transform of IMF, the spectrum and marginal spectrum of the signal can be obtained, and then the unbalanced fault signal of rotor can be analyzed. Secondly, aiming at the endpoint effect of Hilbert-Huang transform in the process of signal empirical mode decomposition, based on the research of the traditional method of suppressing the end point effect, An improved endpoint continuation method based on image extension and support vector regression machine is proposed. The improved method first uses the support vector regression machine to predict the extreme point data sequence of the original signal, and then uses the image continuation method to determine the location of the predicted extremum point. It solves the problems of inaccurate prediction of long data series by support vector regression machine and poor processing effect of image continuation method for short data sequences whose endpoints are not extreme points. The qualitative comparison between the improved method and the traditional endpoint continuation method is carried out through simulation experiments. The results show that the improved method can suppress the endpoint effect generated by the empirical mode decomposition (EMD). Then, the improved Hilbert-Huang transform method is used to diagnose and analyze the unbalanced fault of the motor rotor, and the characteristics of the unbalanced fault signal can be extracted from the obtained IMF diagram and the Hilbert marginal spectrum. Finally, combined with the improved method of Hilbert-Huang transform proposed in this paper, a fault diagnosis system for rotor unbalance of motor based on LabVIEW is designed. Through experimental analysis, the system can effectively diagnose the unbalanced fault of motor rotor.
【学位授予单位】：河南理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM307

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