Health monitoring of cooling fan bearings based on wavelet filter

被引:48
作者
He, Wei [1 ]
Miao, Qiang [2 ]
Azarian, Michael [1 ]
Pecht, Michael [1 ]
机构
[1] Univ Maryland, Ctr Adv Life Cycle Engn, College Pk, MD 20742 USA
[2] Univ Elect Sci & Technol China, Sch Mech Elect & Ind Engn, Chengdu 611731, Sichuan, Peoples R China
关键词
Health monitoring; Cooling fan bearing; Wavelet; Degradation assessment; Fault diagnosis; Time domain index; FREQUENCY RESONANCE TECHNIQUE; ROLLING ELEMENT BEARINGS; MORLET WAVELET; VIBRATION; DIAGNOSIS; HILBERT; SELECTION; SIGNALS; LIFE;
D O I
10.1016/j.ymssp.2015.04.002
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
In this paper, a vibration-based health monitoring approach for cooling fans is proposed using a wavelet filter for early detection of faults in fan bearings and for the assessment of fault severity. To match the wavelet filter to the fault characteristic signal, a fuzzy rule is introduced to maximize the amplitudes of bearing characteristic frequencies (BCFs), which are an indicator of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to capture the bearing degradation trend. A comparative study is conducted with commonly used time-domain indices in the degradation assessment, and performance is quantified by three measures, i.e., monotonicity, prognosability, and trendability. The analysis results of the experimental data show that the proposed method can effectively detect incipient defects and can better capture the degradation trend of fan bearings than traditional time-domain indices in vibration analysis. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:149 / 161
页数:13
相关论文
共 35 条
[1]  
Andhare AB, 2009, ADV VIB ENG, V8, P329
[2]  
Angrisani L., 1999, Measurement, V25, P19, DOI 10.1016/S0263-2241(98)00063-3
[3]  
Angrisani L., 1999, Measurement, V25, P31, DOI 10.1016/S0263-2241(98)00064-5
[4]  
[Anonymous], 2006, IPC9591
[5]  
[Anonymous], 2008, Prognostics and Health Management of Electronics
[6]  
[Anonymous], IEDM
[7]  
[Anonymous], P IEEE AER C SEM BIG
[8]  
Ayyub B.M., 2006, Uncertainty Modeling and Analysis in Engineering and the Sciences
[9]   A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection [J].
Bozchalooi, I. Soltani ;
Liang, Ming .
JOURNAL OF SOUND AND VIBRATION, 2007, 308 (1-2) :246-267
[10]   Sensor Systems for Prognostics and Health Management [J].
Cheng, Shunfeng ;
Azarian, Michael H. ;
Pecht, Michael G. .
SENSORS, 2010, 10 (06) :5774-5797