Planet bearing fault extraction based on parameter optimized maximum second-order cyclostationary blind deconvolution

PDF(1700 KB)

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (2) : 321-328.

LIN Yun, GUO Yu, CHEN Xin

Author information +

History +

Abstract

In the vibration signal of planetary gearbox, the gear related vibration component usually has relatively high energy. At the same time, bearing slip will make it difficult to obtain the characteristic frequency of the vibration component corresponding to planet bearing fault. Therefore, a fault extraction method of planet bearing based on parameter optimized Maximum Second Order Cyclostationary Blind Deconvolution (CYCBD) is proposed. Aiming at the problem that it is difficult for CYCBD to obtain the cycle frequency and filter length under the condition of bearing slip, the improved envelope spectrum fault feature ratio (IFFR) index is used as the fitness function of particle swarm optimization algorithm to automatically obtain the actual cycle frequency and optimize the filter length in CYCBD algorithm, and the parameter adaptive CYCBD algorithm is used to enhance the impact of bearing fault. The square envelope spectrum of deconvolution results reflects the bearing fault characteristics, so as to extract the fault characteristics accurately. The simulation and experimental results show that this method can effectively extract fault features of planet bearing.

Key words

Maximum second order cyclostationary blind deconvolution / Planetary gearbox / Bearing fault diagnosis / Fault characteristic ratio

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIN Yun, GUO Yu, CHEN Xin. Planet bearing fault extraction based on parameter optimized maximum second-order cyclostationary blind deconvolution[J]. Journal of Vibration and Shock, 2023, 42(2): 321-328

References

[1] 祝小彦, 王永杰. 基于MOMEDA与Teager能量算子的滚动轴承故障诊断[J].振动与冲击,2018,37(06):104-110+123.
ZHU Xiaoyan, WANG Yongjie. Fault diagnosis of rolling bearings based on the MOMEDA and Teager energy operator[J]. Journal of Vibration and Shock, 2018,37(06):104-110+123.
[2] MCDONALD G L, ZHAO Q. Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection [J]. Mechanical Systems and Signal Processing, 2017, 82:461-477.
[3] 胡爱军, 严家祥, 白泽瑞. 基于MOMEDA和增强倒频谱的风电机组齿轮箱多故障诊断方法[J]. 振动与冲击,2021,40(07):268-273.
HU Aijun, YAN Jiaxiang, BAI Zerui. Multi-fault diagnosis method for wind turbine gearbox based on MOMEDA and enhanced cepstrum[J]. Journal of Vibration and Shock, 2021,40(07):268-273.
[4] BUZZONI M， ANTONI J， D’ELIA G．Blind deconvolution based on cyclostationarity maximization and its application to fault identification[J]. Journal of Sound and Vibration, 2018, 432:569-601.
[5] 黄包裕, 张永祥, 赵磊. 基于布谷鸟搜索算法和最大二阶循环平稳盲解卷积的滚动轴承故障诊断方法[J]. 机械工程学报, 2021, 57(09):99-107.
HUANG Baoyu, ZHANG Yongxiang, ZHAO Lei. Research on Fault Diagnosis Method of Rolling Bearings Based on Cuckoo Search Algorithm and Maximum Second Order Cyclostationary Blind Deconvolution[J]. Journal of Mechanical Engineering,2021,57(9): 99-107.
[6] 罗忠, 徐迪, 李雷, 马辉. 基于改进二阶循环平稳解卷积的轴承故障检测方法[J].东北大学学报(自然科学版),2021,42(05):673-678.
LUO Zhong, XU Di, LI Lei, et al. Bearing Fault Detection Based on Improved CYCBD Method[J]. Journal of Northeastern University(Natural Science), 2021,42(05):673-
678.
[7] MOSHREFZADEH A. Condition monitoring and intelligent diagnosis of rolling element bearings under constant/variable load and speed conditions[J]. Mechanical Systems and Signal Processing, 2021, 149:107153.
[8] 朱丹宸, 张永祥, 何伟等. 基于自适应CYCBD和互相关谱的滚动轴承复合故障诊断方法[J].振动与冲击,2020,39(11):116-122+128.
ZHU Danchen, ZHANG Yongxiang, HE Wei, et al. Compound faults diagnosis of rolling element bearing using adaptive CYCBD and cross-correlation spectrum[J]. Journal of Vibration and Shock,2020,39(11): 116-122+128.
[9] KENNEDY J，OBAIAHNAHATTI B G. Particle swarm optimization［C］//Proceedings of ICNN′95-International Conference on Neural Networks，November 27-December 1，Perth，WA，Australia. NewYork：IEEE，1995，4：1942-1948.
[10] 唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(05):73-81.
TANG Guiji, WANG Xiaolong. Parameter Optimized Variational Mode Decomposition Method with Application to Incipient Fault Diagnosis of Rolling Bearing[J]. Journal of Xi’an Jiaotong University, 2015,49(05):73-81.
[11] 邵晴. 粒子群算法研究及其工程应用案例[D].吉林大学,2017.
SHAO Qing. Particle Swarm Optimization and Its Application in Engineering[D]. Jilin University, 2017.
[12] RANDALL J. Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm[J]. Mechanical Systems and Signal Processing, 2004.
[13] YANG M , JIN C , DONG G . Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum[J]. Mechanical Systems & Signal Processing, 2011, 25(5):1773-1785.
[14] 冯志鹏, 赵镭镭, 褚福磊. 行星齿轮箱齿轮局部故障振动频谱特征[J]. 中国电机工程学报, 2013, 33(5): 119-127.
FENG Zhipeng, ZHAO Leilei, CHU Fulei. Vibration Spectral Characteristics of Localized Gear Fault of Planetary Gearboxes[J]. Chinese Journal of Electrical Engineering, 2013, 33(5): 119-127.
[15] WANG T，HAN Q，CHU F，et al． A new SKR gram baseddemodulation technique for planet bearing fault detection[J]．Journal of Sound and Vibration，2016，385: 330-349．
[16] RANDALL R B, ANTONI J. Rolling element bearing diagnostics—A tutorial. Mechanical Systems & Signal Processing, 2011, 25(2):485-520.