1. Liren College, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3. School of Electrical Engineering, Hunan Industry Polytechnic, Changsha, Hunan 410208, China
Abstract:In view of the problem that the extraction effect of the traditional stochastic resonance (SR) method for rolling bearing fault feature is unsatisfactory under strong background noise, a fault diagnosis method of rolling bearing based on improved coupling-enhanced SR is proposed. First, the coupled SR system is constructed by using a fixed-parameter bistable system forced by an external input signal and a variable-parameter bistable system. Second, the SR control of coupled system is realized by adjusting the system parameter and coupling coefficient. And the genetic algorithm is used to realize the adaptive selection of the control parameters. Finally, experiments and engineering application are performed to verify the effectiveness and superiority of the proposed method in the rolling bearing fault diagnosis.
[1]钟秉林, 黄仁. 机械故障诊断学[M]. 北京:机械工业出版社, 2007.
[2]Sawalhi N, Randal R B, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2007, 21: 2616-2633.
[3]李宏坤, 杨蕊, 任远杰, 等. 利用粒子滤波与谱峭度的滚动轴承故障诊断[J]. 机械工程学报, 2017, 53(3):63-72.
Li H K, Yang R, Ren Y J, et al. Rolling Element Bearing Diagnosis Using Particle Filter and Kurtogram[J]. Journal of Mechanical Engineering, 2017, 53(3): 63-72.
[4]张晗 ,杜朝辉, 方作为, 等. 基于稀疏分解理论的航空发动机轴承故障诊断[J]. 机械工程学报, 2015, 51(1):97-105.
Zhang H, Du C H, Fang Z W, et al. Sparse Decomposition Based Aero-engine’s Bearing Fault Diagnosis[J]. Journal of Mechanical Engineering, 2015, 51(1):97-105.
[5]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.
[6]孟宗, 李良良. 基于LCD分解和形态学分形维数的滚动轴承故障诊断方法[J]. 计量学报, 2016, 37(3):284-288.
Meng Z, Li L L. Rolling Bearing Fault Diagnosis Based on Local Characterist-scale Decomposition and Morphological Fractal Dimension[J]. Acta Metrologica Sinica, 2016, 37(3): 284-288.
[7]鄢小安, 贾民平. 基于改进奇异谱分解的形态学解调方法及其在滚动轴承故障诊断中的应用[J]. 机械工程学报, 2017, 53(7):104-112.
Yan X A, Jia M P. Morphological Demodulation Method Based on Improved Singular Spectrum Decomposition and Its Application in Rolling Bearing Fault Diagnosis[J]. Journal of echanical Engineering, 2017, 53(7):104-112.
[8]时培明, 梁凯, 赵娜, 等. 基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断[J]. 计量学报, 2018, 39(1):61-65.
Shi P M, Liang K, Zhao N, et al. Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM[J]. Acta Metrologica Sinica, 2018, 39(1): 61-65.
[9]陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断[J]. 仪器仪表学报, 2017, 38(1):33-40.
Chen C, Shen F, Yan R Q. Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis[J]. Chinese Journal of Scientific Instrument, 2017, 38(1):33-40.
[10]Lu S L, He Q B, Zhang H B, et al. Enhanced rotating machine fault diagnosis based on time-delayed feedback stochastic resonance[J]. Journal of Vibration and Acoustics, 2015, 137: 051008_1-12.
[11]时培明, 李培, 韩东颖, 等. 基于变尺度多稳态随机共振的微弱信号检测研究[J]. 计量学报, 2015, 36(6):628-633.
Shi P M, Li P, Han D Y, et al. Study on Weak Signal Detection Based on Variable Scale Multi-stable Stochastic Resonance[J]. Acta Metrologica Sinica, 2015, 36(6): 628-633.
[12]Hu B B, Li B. A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains[J]. Measurement Science and Technology, 2016, 27 (2): 025017_1-14.
[13]李永波, 徐敏强, 赵海洋, 等. 级联双稳随机共振和基于Hermite插值的局部均值分解方法在齿轮故障诊断中的应用[J]. 振动与冲击, 2015, 34(5):95-101.
Li Y B, Xu M Q, Zhao H Y, et al. Application of cascaded bistable stochastic resonance and Hermite interpolation local mean decomposition method in gear fault diagnosis[J]. Journal of Vibration and Shock, 2015, 34(5):95-101.
[14]郑俊, 林敏. 耦合双稳系统随机共振的轴承故障检测方法[J]. 中国计量学院学报, 2014, 25(1):51-56.
Zheng J, Lin M. Detection of bearing fault signals based on coupled bistable system stochastic resonance[J]. Journal of China University of Metrology, 2014, 25(1):51-56.
[15]时培明,苏晓,袁丹真,等. 基于VMD和变尺度多稳随机共振的微弱故障信号特征提取方法[J]. 计量学报, 2018, 39(4): 515-520.
Shi P M, Su X, Yuan D Z. A New Feature Extraction Method of Weak Fault Signal Based on VMD and Re-scaling Multi-stable Stochastic Resonance[J]. Acta Metrologica Sinica, 2018, 39(4): 515-520.