Application of the EEMD method to rotor fault diagnosis of rotating machinery
Application of the EEMD method to rotor fault diagnosis of rotating machinery
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DOI：
10.1016/j.ymssp.2008.11.005
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发表时间：
2009-05-01
影响因子：
8.4
通讯作者：
Zi, Yanyang
中科院分区：
文献类型：
--
作者：
Lei, Yaguo;He, Zhengjia;Zi, Yanyang
经验模式分解（EMD）是一种自适应的非线性、非平稳信号分析方法。它可以根据信号的局部特征时间尺度将复杂信号分解为一组本征模态函数。近年来，经验模态分解方法在旋转机械故障诊断中得到了广泛的应用。但由于存在模混叠问题，它不能准确地反映信号的特征信息。针对经验模态分解（EMD）中存在的模态混叠问题，提出了集合经验模态分解（EEMD）方法. EEMD可以从信号中提取出真正具有物理意义的成分。利用EEMD的优点，提出了一种基于EEMD的旋转机械故障诊断新方法。首先，利用仿真信号对基于EEMD的方法进行了性能测试。将该方法应用于发电机碰摩故障诊断和重油催化裂化机组碰摩故障早期诊断。最后，通过与经验模态分解方法的应用结果比较，验证了基于EEMD方法在旋转机械故障特征信息提取中的优越性。(C)2008爱思唯尔有限公司保留所有权利。
Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and non-stationary signals. It may decompose a complicated signal into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The EMD method has attracted considerable attention and been widely applied to fault diagnosis of rotating machinery recently. However, it cannot reveal the signal characteristic information accurately because of the problem of mode mixing. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new EEMD-based method for fault diagnosis of rotating machinery. First, a simulation signal is used to test the performance of the method based on EEMD. Then, the proposed method is applied to rub-impact fault diagnosis of a power generator and early rub-impact fault diagnosis of a heavy oil catalytic cracking machine set. Finally, by comparing its application results with those of the EMD method, the superiority of the proposed method based on EEMD is demonstrated in extracting fault characteristic information of rotating machinery. (C) 2008 Elsevier Ltd. All rights reserved.