Abstract：In order to solve problems,such as,overdependence on single fault characteristic frequency,and the ambiguity and inefficiency of diagnosis results caused by subjective factors in fault diagnosis of rolling bearings,a method using the envelope spectrum and resonance demodulation analysis of signals was proposed to identify the fault characteristic frequency,and its multiplications and modulation frequency components of rolling bearings.Firstly,the original signal was analyzed with the envelope spectrum or resonance demodulation analysis.Then,a specific algorithm was used to identify faulty frequency components and rotating frequency in the spectrum.At last,the fault diagnosis of rolling bearings was conducted with the corresponding proportions of the identified frequency components.The fault diagnosis simulations and the life acceleration tests of rolling bearings demonstrated the validity of the proposed method.
高大为，朱永生，刘煜炜，曹鹏辉，高闯. 一种滚动轴承特征频率的自动识别方法研究[J]. 振动与冲击, 2017, 36(9): 58-62.
GAO Dawei,ZHU Yongsheng,LIU Yuwei,CAO Penghui,GAO Chuang. An automatic recognition method for characteristic frequency of rolling bearings. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(9): 58-62.
[1] Ferat Sahin, M.Cetin Yavuz,et al. Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization[J]. Parallel Computing, Volume 33, Issue 2, March 2007, Pages 124-143.
[2] Chang, Y.-w., Y.-c, Wang,et al. Fault diagnosis of a mine hoist using PCA and SVM techniques[J]. Journal of China University of Mining and Technology, 2008, 18(3): 327-331.
[3] McFadden P D, Smith J D. Vibration monitoring of rolling element bearing by the high-frequency esonance technique-a review [J]. Int. J. Tribology, 1984, 17: 3 － 10．
[4] 王宏超，陈进，董广明，等. 基于快速kurtogram 算法的共振解调方法在滚动轴承故障特征提取中的应用[J]. 振动与冲击，2013, 32(1):35-38.
WANG Hong-chao, CHEN Jin, DONG Guang-ming,et al. Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of kutrogram [J]. Journal of Vibration and Shock,2013,32 (1): 35－38.
[5] Dwyer R F. Detection of non-gaussian signals by frequency domain kurtosis estima-tion[C]. Acoustic, Speech and Signal Processing. Boston: IEEE International Conference on ICASSP, 1983: 607－610.
[6] Antoni J. Fast computation of the Kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing,2007,21 (1):108－124.
[7] Bo Li,Mo-Yuen Chow,Yodyium Tipsuwan,James C. Hung. Neural-Network-Based Motor Rolling Bearing Fault Diagnosis[J], IEEE Transaction on indusrial electronics,2000,47(5): 1060-1068.
[8] N.Tandon,A.Choudhury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearing [J], Tribology International,1999,32(8):469-480.
[9] 何正嘉, 訾艳阳,张西宁, 等. 现代信号处理及工程应用[M]. 西安：西安交通大学出版社, 2007.
[10] http: ∥www． eecs． cwru． edu /laboratory /bearing /welcome _overview． htm．
[11] Xiaoran Zhu, Youyun Zhang, Yongsheng Zhu. Bearing performance degradation assessment based on the rough support vector data description[J]. Mechanical Systems and Signal Processing, 2013, 34 (1-2): 203-217.
