基于周期势函数的自适应二阶欠阻尼随机共振信号增强方法
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摘要
针对滚动轴承微弱故障振动信号在噪声环境下故障特征难以提取的问题,提出一种基于周期势函数的自适应二阶欠阻尼随机共振信号增强方法。采用粒子群算法对系统参数和阻尼系数的自适应匹配,实现对多个拟增强频段的随机共振,更加适用于工程实际中多故障信号提取。数据库考题检验和工程实验验证表明:1)该方法明显提高了输出信噪比,故障特征频率处主峰突出,边带干扰少,方便故障的机器判读,误判率低; 2)随着噪声强度的增加,虽然输出信噪比有所降低,但该方法的检测效果仍优于基于周期势函数的自适应一阶随机共振方法的检测效果; 3)该方法对噪声的适应性更强,在噪声环境下对于微弱故障信号的提取有着明显优势。
Aiming at the problem that the faulty vibration signal of rolling bearing is difficult to extract under noisy environment,an adaptive second-order underdamped stochastic resonance signal enhancement method based on periodic potential function is proposed. The method uses particle swarm optimization to measure system parameters and damping.The adaptive matching of the coefficients realizes the stochastic resonance of multiple pseudo-enhanced frequency bands,and is more suitable for multi-fault signal extraction in engineering practice. The test of fault signal and the engineering experiment show that: 1) The adaptive second-order under-damped stochastic resonance method based on periodic potential function significantly improves the output signal-to-noise ratio,the main peak of the fault characteristic frequency is prominent. The sideband interference is less,the machine is easy to fault,and the false positive rate is low. 2) With the increase of noise intensity,although the output signal-to-noise ratio is reduced,the adaptive second-order underdamped stochastic resonance method based on periodic potential function,the detection effect is still better than the adaptive firstorder stochastic resonance method based on periodic potential function. 3) The adaptive second-order under-damped stochastic resonance method based on periodic potential function is more adaptable to noise,in noisy environment,there are obvious advantages for the extraction of weak fault signals.
Aiming at the problem that the faulty vibration signal of rolling bearing is difficult to extract under noisy environment,an adaptive second-order underdamped stochastic resonance signal enhancement method based on periodic potential function is proposed. The method uses particle swarm optimization to measure system parameters and damping.The adaptive matching of the coefficients realizes the stochastic resonance of multiple pseudo-enhanced frequency bands,and is more suitable for multi-fault signal extraction in engineering practice. The test of fault signal and the engineering experiment show that: 1) The adaptive second-order under-damped stochastic resonance method based on periodic potential function significantly improves the output signal-to-noise ratio,the main peak of the fault characteristic frequency is prominent. The sideband interference is less,the machine is easy to fault,and the false positive rate is low. 2) With the increase of noise intensity,although the output signal-to-noise ratio is reduced,the adaptive second-order underdamped stochastic resonance method based on periodic potential function,the detection effect is still better than the adaptive firstorder stochastic resonance method based on periodic potential function. 3) The adaptive second-order under-damped stochastic resonance method based on periodic potential function is more adaptable to noise,in noisy environment,there are obvious advantages for the extraction of weak fault signals.
引文
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[10]孟宗,谷伟明,胡猛,等.基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取[J].计量学报,2016,37(4):406-411.Men Z, Gu W M, Hu M, et al. Fault Feature Extraction of Rolling Bearing Incipient Fault Based on Improved Singular Value Decomposition and EMD[J].Acta Metrologica Sinica,2016,37(4):406-411.
[11]张淑清,邢婷婷,何红梅,等.基于VMD及广义分形维数矩阵的滚动轴承故障诊断[J].计量学报,2017,38(4):439-443.Zhang S Q,Xing T T,He H M,et al. Bearing Fault Diagnosis Method Based on VMD and Generalized Fractal Dimension Matrix[J]. Acta Metrologica Sinica,2017,38(4):439-443.
[12]付秀伟,高兴泉.基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法[J].计量学报,2018,39(5):688-692.Fu X W,Gao X Q. Rolling Bearing Fault Diagnosis Based on FDM and Singular Value Difference Spectrum[J]. Acta Metrologica Sinica,2018,39(5):688-692.
[13]李一博,王会芳,张博林.基于势函数参数的随机共振性能研究[J].纳米技术与精密工程,2017,15(2):114-120.Li Y B,Wang H F,Zhang B L. Stochastic resonance based on potential function parameter[J].Nanotechnology and Precision Engineering,2017,15(2):114-120.
[2]时培明,丁雪娟,韩东颖.基于变尺度频移带通随机共振的多频微弱信号检测方法[J].计量学报,2013,34(3):282-288.Shi P M,Ding X J, Han D Y. Detection of multifrequency weak signals based on re-scaling frequencyshifted band-pass stochastic resonance[J]. Acta Metrologica Sinica,2013,34(3):282-288.
[3]樊养余,李利品,党瑞荣.基于随机共振的任意频率微弱信号检测方法研究[J].仪器仪表学报,2013,34(3):567-572.Fan Y Y,Li L P,Dang R R. Study on high frequency weak signal detection method based on stochastic resonance[J]. Chinese Journal of Scientific Instrument,2013,34(3):567-572
[4] Tan J,Chen X,Wang J,et al. Study of frequencyshifted and re-scaling stochastic resonance and its application to fault diagnosis[J]. Mechanical System and Signal Processing,2009,23(3):811-822.
[5]胡茑庆.随机共振微弱特征信号检测理论与方法[M].北京:国防工业出版社,2012:42-53.Hu N Q. Stochastic resonance weak characteristic signal detection theory and methods[M]. Beijing:National Defense Industry Press,2012:42-53
[6] Huang D,Yang J,Zhang J,et al. An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2017,232(13):2352-2368.
[7] Liu X,Liu H,Yang J,et al. Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system[J]. Mechanical Systems and Signal Processing,2017,96:58-76.
[8] Lopez L,Zhong W,Lu S,et al. Stochastic resonance in an underdamped system with Fitz Hug-Nagumo potential for weak signal detection[J]. Journal of Sound and Vibration,2017,411:34-46.
[9] Lei Y,Qiao Z,Xu X,et al. An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2017,94:148-164.
[10]孟宗,谷伟明,胡猛,等.基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取[J].计量学报,2016,37(4):406-411.Men Z, Gu W M, Hu M, et al. Fault Feature Extraction of Rolling Bearing Incipient Fault Based on Improved Singular Value Decomposition and EMD[J].Acta Metrologica Sinica,2016,37(4):406-411.
[11]张淑清,邢婷婷,何红梅,等.基于VMD及广义分形维数矩阵的滚动轴承故障诊断[J].计量学报,2017,38(4):439-443.Zhang S Q,Xing T T,He H M,et al. Bearing Fault Diagnosis Method Based on VMD and Generalized Fractal Dimension Matrix[J]. Acta Metrologica Sinica,2017,38(4):439-443.
[12]付秀伟,高兴泉.基于傅里叶分解与奇异值差分谱的滚动轴承故障诊断方法[J].计量学报,2018,39(5):688-692.Fu X W,Gao X Q. Rolling Bearing Fault Diagnosis Based on FDM and Singular Value Difference Spectrum[J]. Acta Metrologica Sinica,2018,39(5):688-692.
[13]李一博,王会芳,张博林.基于势函数参数的随机共振性能研究[J].纳米技术与精密工程,2017,15(2):114-120.Li Y B,Wang H F,Zhang B L. Stochastic resonance based on potential function parameter[J].Nanotechnology and Precision Engineering,2017,15(2):114-120.