School of General Education, Shenyang Sport University, Shenyang 110115, China.
Department of Mechanical Engineering, University of Cincinnati, Cincinnati 45221, USA.
Comput Intell Neurosci. 2022 Apr 25;2022:2698498. doi: 10.1155/2022/2698498. eCollection 2022.
Because of the nonlinearity and nonstationarity in the vibration signals of some rotating machinery, the analysis of these signals using conventional time- or frequency-domain methods has some drawbacks, and the results can be misleading. In this paper, a couple of features derived from multivariate empirical mode decomposition (MEMD) are introduced, which overcomes the shortcomings of the traditional features. A wind turbine gearbox and its bearings are investigated as rotating machinery. In this method, two types of feature structures are extracted from the decomposed signals resulting from the MEMD algorithm, called intrinsic mode function (IMF). The first type of feature vector element is the energy moment of effective IMFs. The other type of vector elements is amplitudes of a signal spectrum at the characteristic frequencies. A correlation factor is used to detect effective IMFs and eliminate the redundant IMFs. Since the basic MEMD algorithm is sensitive to noise, a noise-assisted extension of MEMD, NA-MEMD, is exploited to reduce the effect of noise on the output results. The capability of the proposed feature vector in health condition monitoring of the system is evaluated and compared with traditional features by using a discrimination factor. The proposed feature vector is utilized in the input layer of the classical three-layer backpropagation neural network. The results confirm that these features are appropriate for intelligent fault detection of complex rotating machinery and can diagnose the occurrence of early faults.
由于某些旋转机械的振动信号具有非线性和非平稳性，因此使用传统的时频域方法对这些信号进行分析存在一些缺点，并且结果可能会产生误导。在本文中，介绍了几种源于多元经验模态分解（MEMD）的特征，这些特征克服了传统特征的缺点。以风力涡轮机齿轮箱及其轴承作为旋转机械进行研究。在该方法中，从 MEMD 算法产生的分解信号中提取了两种类型的特征结构，称为固有模态函数（IMF）。特征向量元素的第一种类型是有效 IMF 的能量矩。另一种类型的向量元素是特征频率处信号频谱的幅度。相关系数用于检测有效 IMF 并消除冗余 IMF。由于基本 MEMD 算法对噪声敏感，因此利用噪声辅助扩展 MEMD（NA-MEMD）来减少噪声对输出结果的影响。通过使用判别因子评估了该特征向量在系统健康状况监测中的能力，并与传统特征进行了比较。所提出的特征向量用于经典三层反向传播神经网络的输入层。结果证实，这些特征适用于复杂旋转机械的智能故障检测，可以诊断早期故障的发生。
