Data Mining Based Full Ceramic Bearing Fault Diagnostic System Using AE Sensors

被引：39
|
作者：
He, David [1 ]
Li, Ruoyu [1 ]
Zhu, Junda [1 ]
Zade, Mikhail [1 ]
机构：
[1] Univ Illinois, Dept Mech & Ind Engn, Intelligent Syst Modeling & Dev Lab, Chicago, IL 60607 USA
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 12期
关键词：
Data mining; fault diagnosis; full ceramic bearings; EMPIRICAL MODE DECOMPOSITION; ARTIFICIAL NEURAL-NETWORKS; ROLLING ELEMENT BEARINGS; SUPPORT VECTOR MACHINES; ACOUSTIC-EMISSION; HILBERT SPECTRUM; VIBRATION; ALGORITHM; GEARS;
D O I：
10.1109/TNN.2011.2169087
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Full ceramic bearings are considered the first step toward full ceramic, oil-free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data.
引用
收藏
页码：2022 / 2031
页数：10
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