An improved empirical wavelet transform method for rolling bearing fault diagnosis
- PDF / 1,391,050 Bytes
- 10 Pages / 595.276 x 793.701 pts Page_size
- 22 Downloads / 222 Views
improved empirical wavelet transform method for rolling bearing fault diagnosis 1
1
2
2
2
3
HUANG HaiRun , LI Ke , SU WenSheng , BAI JianYi , XUE ZhiGang , ZHOU Lang , 1,4* 5 SU Lei & PECHT Michael 1
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering,
Jiangnan University, Wuxi 214122, China; Jiangsu Province Special Equipment Safety Supervision Inspection Institute, Wuxi 214071, China; 3 HUST-Wuxi Research Institute, Wuxi 214071, China; 4 State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, 2
5
Wuhan 430074, China; Center for Advanced Life Cycle Engineering, University of Maryland, College Park MD 20742, USA Received November 2, 2019; accepted January 6, 2020; published online April 16, 2020
Empirical wavelet transform (EWT) based on the scale space method has been widely used in rolling bearing fault diagnosis. However, using the scale space method to divide the frequency band, the redundant components can easily be separated, causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively. This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set. The frequency domain feature parameter set includes two characteristic parameters: mean and variance. After adaptively dividing the frequency band by the scale space method, the mean and variance of each band are calculated. Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis. An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode. The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes. The empirical mode with the largest margin factor is selected to envelope spectrum analysis. Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods. fault diagnosis, empirical wavelet transform, scale space method, feature parameter, margin factor Citation:
1
Huang H R, Li K, Su W S, et al. An improved empirical wavelet transform method for rolling bearing fault diagnosis. Sci China Tech Sci, 2020, 63, https://doi.org/10.1007/s11431-019-1522-1
Introduction
A rolling bearing is a precision standard machine component that is widely used in mechanical equipment. According to relevant information, 21% of the total number of mechanical failures are caused by damaged rolling bearings. In ma-
chinery and equipment, due to heavy loads and non-stationary conditions, bearings and other important mechanical parts are inevitably damaged after long-term operation [1–3]. How to effectively detect and diagnose faults in rolling bearings has become a hot research topic [4]. Vibration analysis is considered as an effective method f
Data Loading...
