欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对随机噪声和局部强干扰影响经验模态分解（Empirical mode decomposition，EMD）质量的问题，提出一种形态奇异值分解滤波消噪方法，并将其与EMD相结合形成一种新的故障特征提取方法。该方法首先对原始振动信号进行相空间重构和奇异值分解（Singular value decomposition，SVD），根据奇异值分布曲线确定降噪阶次进行SVD降噪，再形态滤波，最后把消噪后的信号进行EMD分解，利用本征模模态分量（Intrinsic mode function，IMF）提取故障特征信息。对仿真信号和实际轴承故障数据的应用分析表明，该方法能有效地提取轴承故障特征，诊断轴承故障，还可以减少EMD的分解层数和边界效应，提高EMD分解的时效性和精确度。
英文摘要：
Due to the influence caused by random noises and local strong disturbances embedded in signal on empirical mode decomposition （EMD） results, a novel integrated singular value decomposition-morphology filter method is proposed to overcome this shortcoming. And combining with EMD, a feature extraction method is presented. Firstly, reconstruct the original vibration signal in phase space and decompose the attractor track matrix by singular value decomposition （SVD）, and then select a reasonable order for noise reduction according to the singular curve. Secondly, filter the de-noised signal by morphology filter. Finally, decompose it by EMD to extract the intrinsic mode functions （IMF） for fault feature extraction. Experimental results and industrial measurement analysi show that this method can extract fault characteristics of roiling bearing effectively, reduce decomposition levels and boundary effect of EMD, and imporve the timeliness and precision thereof.
同期刊论文项目
同项目期刊论文
Dynamic characteristic analysis and experimental study on a new type reducer with small tooth number
Method of improving precision of Hilbert-Huang transform based modal parameter identification for sh
位置：