Roller element bearing fault diagnosis using singular spectrum analysis
Roller element bearing fault diagnosis using singular spectrum analysis
复制标题
DOI：
10.1016/j.ymssp.2012.08.019
复制
发表时间：
2013-02-01
影响因子：
8.4
通讯作者：
Murty, S. A. V. Satya
中科院分区：
文献类型：
--
作者：
Muruganatham, Bubathi;Sanjith, M. A.;Murty, S. A. V. Satya
现有的时间序列特征提取方法大多涉及复杂的算法，提取的特征受到样本大小和噪声的影响。本文提出了一种利用振动信号奇异谱分析提取轴承故障特征的简单时间序列方法。该方法易于实现，故障特征具有抗噪性。SSA用于将采集到的信号分解成主成分的加性集合。提出了一种新的主成分选择方法。实现了两种基于SSA的特征提取方法。在第一种方法中，所选择的奇异值（SV）数作为故障特征，并在第二种方法中，所选择的SV数对应的主成分的能量作为特征。采用人工神经网络进行故障诊断。该算法进行了评估，使用两个实验数据集，一个从电机轴承受到不同的故障严重程度在各种负载，有和没有噪音，和其他轴承振动数据中获得的齿轮箱的存在。研究了样本容量、故障大小和载荷对故障特征的影响。与现有的时间序列方法相比，所提出的方法的优点进行了讨论。实验结果表明，该方法具有简单、抗噪和高效的特点。(C)2012爱思唯尔有限公司保留所有权利。
Most of the existing time series methods of feature extraction involve complex algorithm and the extracted features are affected by sample size and noise. In this paper, a simple time series method for bearing fault feature extraction using singular spectrum analysis (SSA) of the vibration signal is proposed. The method is easy to implement and fault feature is noise immune. SSA is used for the decomposition of the acquired signals into an additive set of principal components. A new approach for the selection of the principal components is also presented. Two methods of feature extraction based on SSA are implemented. In first method, the singular values (SV) of the selected SV number are adopted as the fault features, and in second method, the energy of the principal components corresponding to the selected SV numbers are used as features. An artificial neural network (ANN) is used for fault diagnosis. The algorithms were evaluated using two experimental datasets one from a motor bearing subjected to different fault severity levels at various loads, with and without noise, and the other with bearing vibration data obtained in the presence of a gearbox. The effect of sample size, fault size and load on the fault feature is studied. The advantages of the proposed method over the exiting time series method are discussed. The experimental results demonstrate that the proposed bearing fault diagnosis method is simple, noise tolerant and efficient. (C) 2012 Elsevier Ltd. All rights reserved.