Multistable matching stochastic resonance with its application to mechanical incipient fault characteristic extraction
QIAO Zijian1,2,3,4, CHEN Shuai1, MA Li5, LAI Zhihui6, LIU Jian7,XU Xuefang8, MEI Yidan9
1.School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China;
2.State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China;
3.Yangjiang Research Center Advanced Energy Science and Technology Guangdong Laboratory (Yangjiang Offshore Wind Power Laboratory), Yangjiang 529500, China;
4.Zhejiang Provincial Key Laboratory of Part Rolling Technology, Ningbo 315211, China;
5.School of Energy and Control Engineering，Changji College, Changji 831100, China;
6.College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China;
7.College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China;
8.School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
9.CATARC-NB, Ningbo 315104, China
Abstract：It is difficult for traditional monostable and bistable stochastic resonance (SR) methods to match with complicated and variable input signals due to the single stable structure of the potential function. Moreover, the scaling factor is generally fixed as a constant, thereby neglecting the synergistic effect among the potential structure, input signal and scaling factor. In addition, since traditional SR methods are based on first-order model, they easily suffer from the noise interference in the low frequency, thereby strongly depending on the help of highpass filter. Therefore, an adaptive second-order multistable matching SR method is proposed to extract weak characteristics in mechanical incipient faults. The method has three advantages: (1) the diversity of multistable potential structure is able to achieve the adaptive matching with different input signals, (2) the optimal synergistic effect among input signal, potential structure and scaling factor is obtained and (3) second-order multistable SR is able to suppress the noise interference in the low frequency. Finally, the proposed method is validated using simulated and experimental data. The results show that the proposed method is able to not only extract weak characteristics from the original vibration signals, but also possess stronger enhancment performance than the traditional methods.
谯自健1,2,3,4,陈帅1,马莉5,赖志慧6,刘健7,许学方8,梅一丹9. 多稳态匹配随机共振在机械早期故障特征提取中的应用[J]. 振动与冲击, 2023, 42(11): 87-95.
QIAO Zijian1,2,3,4, CHEN Shuai1, MA Li5, LAI Zhihui6, LIU Jian7,XU Xuefang8, MEI Yidan9. Multistable matching stochastic resonance with its application to mechanical incipient fault characteristic extraction. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(11): 87-95.
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