中国物理B ›› 2023, Vol. 32 ›› Issue (5): 50202-050202.doi: 10.1088/1674-1056/aca4bd

• • 上一篇    下一篇

Weak signal detection method based on novel composite multistable stochastic resonance

Shangbin Jiao(焦尚彬)1, Rui Gao(高蕊)1,2,†, Qiongjie Xue(薛琼婕)1, and Jiaqiang Shi(史佳强)1   

  1. 1 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China;
    2 School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China
  • 收稿日期:2022-10-27 修回日期:2022-11-14 接受日期:2022-11-22 出版日期:2023-04-21 发布日期:2023-05-05
  • 通讯作者: Rui Gao E-mail:gaorui@bjwlxy.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 61871318), the Key Research and Development Projects in Shaanxi Province (Grant No. 2023-YBGY-044), and the Key Laboratory System Control and Intelligent Information Processing (Grant No. 2020CP10).

Weak signal detection method based on novel composite multistable stochastic resonance

Shangbin Jiao(焦尚彬)1, Rui Gao(高蕊)1,2,†, Qiongjie Xue(薛琼婕)1, and Jiaqiang Shi(史佳强)1   

  1. 1 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China;
    2 School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China
  • Received:2022-10-27 Revised:2022-11-14 Accepted:2022-11-22 Online:2023-04-21 Published:2023-05-05
  • Contact: Rui Gao E-mail:gaorui@bjwlxy.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 61871318), the Key Research and Development Projects in Shaanxi Province (Grant No. 2023-YBGY-044), and the Key Laboratory System Control and Intelligent Information Processing (Grant No. 2020CP10).

RichHTML

2

PDF (PC)

229

赞

摘要： The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism. We propose a novel composite multistable stochastic-resonance (NCMSR) model combining the Gaussian potential model and an improved bistable model. Compared with the traditional multistable stochastic resonance method, all the parameters in the novel model have no symmetry, the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system parameters. The model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation, the NCMSR model has a higher utilization of noise. Taking the output signal-to-noise ratio as the index, weak periodic signal is detected based on the NCMSR model in Gaussian noise and α noise environment respectively, and the detection effect is good. The application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer race. The outstanding advantages of this method in weak signal detection are verified, which provides a theoretical basis for industrial practical applications.

关键词: weak signal detection, composite multistable, stochastic resonance, bearing fault detection

Abstract: The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism. We propose a novel composite multistable stochastic-resonance (NCMSR) model combining the Gaussian potential model and an improved bistable model. Compared with the traditional multistable stochastic resonance method, all the parameters in the novel model have no symmetry, the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system parameters. The model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation, the NCMSR model has a higher utilization of noise. Taking the output signal-to-noise ratio as the index, weak periodic signal is detected based on the NCMSR model in Gaussian noise and α noise environment respectively, and the detection effect is good. The application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer race. The outstanding advantages of this method in weak signal detection are verified, which provides a theoretical basis for industrial practical applications.

Key words: weak signal detection, composite multistable, stochastic resonance, bearing fault detection

中图分类号:  (Numerical simulation; solution of equations)

  • 02.60.Cb
05.40.-a (Fluctuation phenomena, random processes, noise, and Brownian motion) 05.40.Fb (Random walks and Levy flights) 05.45.-a (Nonlinear dynamics and chaos)