Bearing Fault Detection in Varying Operational Conditions based on Empirical Mode Decomposition and Random Forest

被引：4
|
作者：
Liu, Guozeng [1 ]
Li, Haiping [1 ]
Liu, Wei [2 ]
机构：
[1] Army Engn Univ, Shijiazhuang, Hebei, Peoples R China
[2] Air Force Mil Representat Off, Nanjing, Jiangsu, Peoples R China
来源：
2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018) | 2018年
关键词：
feature extraction; pattern recognition; varying operational conditions; empirical mode decomposition; auto-regressive model; random forest; SUPPORT VECTOR MACHINE; WAVELET PACKET DECOMPOSITION; HILBERT-HUANG TRANSFORM; DIAGNOSTICS;
D O I：
10.1109/PHM-Chongqing.2018.00152
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Roller bearings play a significant role in kinds of machine. In most cases, it won't work in steadily operational conditions. The paper proposed a method which combines empirical mode decomposition and auto-regressive model to extract features of faults in various operational conditions and uses random forests to set an effective pattern recognition model. In addition, the paper compares the result of random forests with that of some other classification method. The bearing vibration data comes from Case Western Reserve University Bearing Data Center. The result indicates that the method is effective and can be used in actual situations.
引用
收藏
页码：851 / 854
页数：4
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