点击次数：

论文类型：期刊论文

发表时间：2013-12-01

发表刊物：JOURNAL OF VIBROENGINEERING

收录刊物：SCIE、Scopus

卷号：15

期号：4

页面范围：1913-1926

ISSN号：1392-8716

关键字：Hilbert spectrum; quantitative analysis; multi-scale entropy; support vector machine; condition classification

摘要：Vibration signal contains much information about machine operational condition and has been broadly used on equipments pattern recognition. Time-frequency distribution is more suitable for nonstationary signals analysis as it provides much information both in time domain and frequency domain. A new method for machine operating condition identification is presented based on the research of Hilbert spectrum (HS) and Multi-Scale Entropy (MSE) for quantitative analysis. Firstly, HS is constructed according to the monitored signals. Then, two-dimension matrix of HS is converted to one-dimension vector. MSE can be calculated as a feature characteristic. The optimal scale will be selected by comparing MSE curves distribution for different working conditions. The sampling entropy on the selected scale and the average energy of HS are combined to construct characteristic vector. In the end, support vector machine (SVM) is used for different working conditions classification by the constructed characteristic vector. In order to verify the effectiveness of this method, experiment of different rolling bearing conditions classification is implemented in the lab. Four different operating conditions of rolling bearing can be effectively indentified by using the above method. It can be concluded that this promising method will contribute to machine condition monitoring and fault diagnosis process.