Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

被引:109
作者
He, David [1 ]
Li, Ruoyu [1 ]
Zhu, Junda [1 ]
机构
[1] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA
关键词
Ball bearing; condition monitoring; data mining; fault detection; fault diagnostics; pattern recognition; vibration; EMPIRICAL MODE DECOMPOSITION; ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINES; WAVELET TRANSFORM; VIBRATION;
D O I
10.1109/TIE.2012.2192894
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Plastic bearings are widely used in medical applications, food processing industries, and semiconductor industries. However, no research on plastic bearing fault diagnostics using vibration sensors has been reported. In this paper, a two-step data mining-based approach for plastic bearing fault diagnostics using vibration sensors is presented. The two-step approach utilizes envelope analysis and empirical mode decomposition (EMD) to preprocess vibration signals and extract frequency domain and time domain fault features as condition indicators (CIs) for plastic bearing fault diagnosis. In the first step, the frequency domain CIs are used by a statistical classification model to identify bearing outer race faults. In the second step, the time domain CIs extracted using EMD are developed to build a k-nearest neighbor algorithm-based fault classifier to identify other types of bearing faults. Seeded fault tests on plastic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and real vibration signals are collected. The effectiveness of the presented fault diagnostic approach is validated using the plastic bearing seeded fault testing data.
引用
收藏
页码:3429 / 3440
页数:12
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