Bearing remain life prediction based on weighted complex SVM models
Bearing remain life prediction based on weighted complex SVM models
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基于加权复杂SVM模型的轴承剩余寿命预测
DOI：
10.21595/jve.2016.16910
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发表时间：
2016-09-01
影响因子：
1
通讯作者：
Wei, Hanbing
中科院分区：
文献类型：
--
作者：
Dong, Shaojiang;Sheng, Jinlu;Wei, Hanbing
为实现轴承剩余寿命预测，提出了一种基于加权复数支持向量机模型的轴承剩余寿命预测方法。首先采用时频域、时频域的方法进行特征提取，从而提取出原始特征。然而，由于提取的原始特征仍然具有高维、包含冗余信息的特点，采用多特征融合技术的主成分分析(PCA)对特征进行融合，进行降维处理。基于第一主成分构造轴承退化指示器，能够准确地指示轴承早期故障状态。然后，基于寿命指标，采用加权复合支持向量机模型实现轴承剩余寿命预测，在该模型中，采用粒子群算法(PSO)选择支持向量机的内部参数，相空间重构算法确定支持向量机的结构。对实际案例进行了分析，结果证明了该方法的有效性。
Aiming to achieve the bearing remaining life prediction, this research proposed a method based on the weighted complex support vector machine (SVM) model. Firstly, the features are extracted by time domain, time-frequency domain method, so as the extract the original features. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique principal component analysis (PCA) is used to merge the features and reduce the dimension. And the bearing degradation indicator is constructed based on the first principal component, which can indicate the bearing early failure state precisely. Then, based on the life condition indicator, the weighted complex SVM model is used to achieve the bearing remain life prediction, in this model, the particle swarm algorithm (PSO) method is used to select the SVM internal parameters, the phase space reconstruction algorithm is used to determine the structure of the SVM. Cases of actual were analyzed, the results proved the effectiveness of the methodology.