EMD与cICA方法在多级齿轮传动微弱故障特征提取中的应用

发布时间：2018-01-10 09:37

本文关键词：EMD与cICA方法在多级齿轮传动微弱故障特征提取中的应用　出处：《机械科学与技术》2017年07期 　论文类型：期刊论文

【摘要】：为提取多级齿轮传动单通道测量信号中隐含的微弱低频故障特征信息,提出了一种基于经验模态分解(Empirical mode decomposition,EMD)与约束独立分量分析(Constrained independent component analysis,cICA)相结合的故障特征提取方法。首先对实测的齿轮箱单通道测量信号进行EMD分解;然后计算各个本征模态函数(Intrinsic mode function,IMF)的峭度及其与原信号的互相关系数,并选择合适的IMFs分量与原信号组成新的虚拟观测向量;最后,通过构建合适的参考信号进行cICA分析,提取出了理想的微弱低频故障特征。通过多级齿轮传动中的低速级断齿故障特征提取试验分析,验证了该方法的有效性和适用性。
[Abstract]:In order to extract the weak low-frequency fault characteristic information from the single-channel measurement signal of multi-stage gear transmission. An empirical mode decomposition based on empirical mode decomposition is proposed. EMD) and constrained independent component analysis. The fault feature extraction method combined with cICA. Firstly, the measured single-channel measurement signal of the gearbox is decomposed by EMD. Then the kurtosis of each intrinsic mode function IMF and its correlation with the original signal are calculated. The suitable IMFs component and the original signal are selected to form the new virtual observation vector. Finally, the ideal weak low-frequency fault features are extracted by constructing appropriate reference signals for cICA analysis, and the low-speed broken tooth fault characteristics of multi-stage gear transmission are extracted and analyzed experimentally. The validity and applicability of the method are verified.
【作者单位】：河南理工大学机械与动力工程学院;
【基金】：国家自然科学基金项目(U1304523) 河南理工大学博士基金项目(B2017-28)资助
【分类号】：TH132.41
【正文快照】： 多级齿轮传动可以实现低转速、大扭矩输出,在矿山机械、化工机械、冶金等领域广泛应用,其低速级齿轮副一旦出现轮齿断裂等局部性故障时,因高速级齿轮啮合振动信号能量大,低速级振动能量弱,且因噪声等因素的干扰,导致测量信号中隐含的周期冲击成分很微弱,其故障特征提取更是一

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