Study of stochastic resonance for bearing fault detection based on fruit fly optimization algorithm

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (10) : 96-100.

CUI Wei-cheng LI Wei MENG Fan-lei LIU Lin-mi

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Abstract

The traditional adaptive stochastic resonance can only realize one-parameter optimization, and the swarm-aptitude optimization algorithms need to choice parameters and convergence speed slowing down with the increase of population. In order to avoid the disadvantages，a new adaptive stochastic resonance method based on fruit fly optimization algorithm(FOA) was proposed．The output signal to noise ratio of a bi-stable system was taken as a fitness function of FOA algorithm, and the parameters were selected adaptively. The analysis of the simulation data and the bearing fault data shows that the new adaptive stochastic resonance method can effectively realize the characteristic signal detection and early fault diagnosis effectively.

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CUI Wei-cheng LI Wei MENG Fan-lei LIU Lin-mi. Study of stochastic resonance for bearing fault detection based on fruit fly optimization algorithm[J]. Journal of Vibration and Shock, 2016, 35(10): 96-100

References

[1] Benzi R, Sutera A, Vulpinai A. The mechanism of stochastic resonance [J]. Journal of Physics A: Mathematical and General, 1981, 14( 11) : 453 -457.
[2] Mcnamara B, Wiesenfeld K, Roy R. Observation of Stochastic Resonance in a Ring Laser [J]. Physical Review Letters, 1988, 60( 25) : 2626-2629.
[3] LI Qiang, WANG Taiyong, LENG Yonggang, et al.Engineering signal processing based on adaptive step-changed stochastic resonance [J]. Mechanical Systemsand Signal Processing, 2007, 21(5): 2267-2279.
[4] LI Jian-long，XU Bo-hou． Effects of signal spectrum varying on signal processing by parameter-induced stochastic resonance［J］. Physica A: Statistical Mechanics and its Applications, 2006, 361( 1) : 11-23．
[5] 冷永刚，王太勇．二次采样用于随机共振从强噪声中提取弱信号的数值研究[J]．物理学报，2003，52( 10) ：2432 -2437．
LENG Yong-gang, WANG Tai-yong. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy Noise [J]. Acta Physica Sinica, 2003, 52( 10) : 2432-2437.
[6] 雷亚国，韩冬，林京，等．自适应随机共振新方法及其在故障诊断中的应用［J］．机械工程学报，2012，48( 7) : 62-67．
LEI Ya-guo，HAN Dong，LIN Jing，et al． New adaptive stochastic resonance method and its application to fault diagnosis [J]. Journal of Mechanical Engineering, 2012,, 48( 7) : 62-67.
[7] 陈敏，胡茑庆，秦国军，等．参数调节随机共振在机械系统早期故障检测中的应用 [J]．机械工程学报，2009，45( 4) : 131-135．
CHEN Min, HU Niao-qing, QIN Guo-jun, et al. Application of parameter-tuning stochastic resonance for detecting early mechanical faults [J]. Journal of Mechanical Engineering, 2009, 45( 4) : 131-135.
[8] 王晶，张庆，梁霖，等．采用遗传算法的自适应随机共振系统弱信号检测方法研究 [J]．西安交通大学学报，2010，44( 3) : 32 -36．
WANG Jing, ZHANG Qing, LIANG Lin, et al. Adaptive stochastic resonance based on genetic algorithm with applications in weak signal detection [J]. Journal of Xian Jiaotong University, 2010, 44( 3) : 32-36.
[9] 张仲海，王多，王太勇，等．采用粒子群算法的自适应变步长随机共振研究[J]．振动与冲击，2013，32(19):125-130．
ZHANG Zhong-hai, WANG Duo, WANG Tai-yong, et al. Self-adaptive step-changed stochastic resonance using particle swarm optimization. Journal of Vibration and Shock, 2013, 32(19):125-130.
[10] 朱维娜，林敏．基于人工鱼群算法的轴承故障随机共振自适应检测方法 [J]．振动与冲击，2014，33(6):143-147．
ZHU Wei-na, LIN Min. Method of adaptive stochastic resonance for bearing fault detection based on artificial fish swarm algorithm [J]. Journal of Vibration and Shock, 2014, 33(6):143-147.
[11] Pan Wen-chao. A new fruit fly optimization algorithm: taking the financial distress model as an example [J].Knowledge-Based System, 2012, 29:69-74.
[12] Case Western Reserve University Bearing Data Center [EB/OL]. http: //csegroups.case.edu/bearingdatacenter.