阿达布思
希尔伯特-黄变换
模式识别(心理学)
人工智能
Boosting(机器学习)
特征提取
滚动轴承
断层(地质)
人工神经网络
工程类
分类器(UML)
火车
计算机科学
方位(导航)
振动
计算机视觉
声学
地图学
滤波器(信号处理)
物理
地质学
地震学
地理
作者
Guorong Cai,Changwei Yang,Y. Pan,Jiaojiao Lv
出处
期刊:Discrete and Continuous Dynamical Systems - Series S
[American Institute of Mathematical Sciences]
日期:2019-01-01
卷期号:12 (4-5): 1471-1487
被引量:7
标识
DOI:10.3934/dcdss.2019101
摘要
Rolling bearings are the most prone components to failure in urban rail trains, presenting potential danger to cities and their residents. This paper puts forward a rolling bearing fault diagnosis method by integrating empirical mode decomposition (EMD) and genetic neural network adaptive boosting (GNN-AdaBoost). EMD is an excellent tool for feature extraction and during which some intrinsic mode functions (IMFs) are obtained. GNN-AdaBoost fault identification algorithm, which uses genetic neural network (GNN) as sub-classifier of the boosting algorithm, is proposed in order to address the shortcomings in classification when only using a GNN. To demonstrate the excellent performance of the approach, experiments are performed to simulate different operating conditions of the rolling bearing, including high speed, low speed, heavy load and light load. For de-nosing signal, by EMD decomposition is applied to obtain IMFs, which is used for extracting the IMF energy feature parameters. The combination of IMF energy feature parameters and some time-domain feature parameters are selected as the input vectors of the classifiers. Finally, GNN-AdaBoost and GNN are applied to experimental examples and the identification results are compared. The results show that GNN-AdaBoost offers significant improvement in rolling bearing fault diagnosis for urban rail trains when compared to GNN alone.
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