Mechanical Fault Feature Extraction under Underdamped Conditions Based on Unsaturated Piecewise Tri-Stable Stochastic Resonance

被引:5
|
作者
Zhao, Shuai [1 ,2 ]
Shi, Peiming [1 ]
机构
[1] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China
[2] Nanchang Inst Sci & Technol, Sch Informat & Artificial Intelligence, Nanchang 330108, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 02期
基金
中国国家自然科学基金;
关键词
stochastic resonance; unsaturated system; underdamped conditions; feature extraction; SYSTEM;
D O I
10.3390/app13020908
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In the case of the rapid development of large machinery, the research of mechanical fault signal feature extraction is of great significance, it can not only ensure the development of the economy but also ensure safety. Stochastic resonance (SR) is of widespread use in feature extraction of mechanical fault signals due to its excellent signal extraction capability. Compared with an overdamped state, SR in an underdamped state is equivalent to one more filtering of the signal, so the signal-to-noise ratio (SNR) of the output signal will be further improved. In this article, based on the piecewise tri-stable SR (PTSR) obtained from previous studies, the feature extraction of mechanical fault signals is carried out under underdamped conditions, and it is found that the SNR of the output signal is further improved. The simulation signals and experimental signals are used to verify that PTSR has better output performance under underdamped conditions.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] A Novel Spectrum Sensing Method Based on Tri-Stable Stochastic Resonance and Quantum Particle Swarm Optimization
    Jin Lu
    Ming Huang
    Jing-Jing Yang
    Wireless Personal Communications, 2017, 95 : 2635 - 2647
  • [42] A weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance
    Wang Yi
    Jiao Shangbin
    Zhang Qing
    Lei Shuang
    Qiao Xiaoxue
    CHINESE JOURNAL OF PHYSICS, 2018, 56 (03) : 1187 - 1198
  • [43] Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction
    Qin, Yi
    Tao, Yi
    He, Ye
    Tang, Baoping
    JOURNAL OF SOUND AND VIBRATION, 2014, 333 (26) : 7386 - 7400
  • [44] Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance
    Yang, Zhen
    Li, Zhiqian
    Zhou, Fengxing
    Ma, Yajie
    Yan, Baokang
    SENSORS, 2022, 22 (17)
  • [45] Bearing Fault Diagnosis Based on Unsaturated Piecewise Non-linear Bistable Stochastic Resonance under Trichotomous Noise
    Zhang, Gang
    Hu, Dayun
    Zhang, Tianqi
    FLUCTUATION AND NOISE LETTERS, 2020, 19 (03):
  • [46] Tri-stable stochastic resonance coupling system driven by dual-input signals and its application in bearing fault detection
    Zhang, Gang
    Zeng, Yujie
    He, Lifang
    PHYSICA SCRIPTA, 2022, 97 (04)
  • [47] Research and Application of Two-Dimensional Time-Delayed Tri-Stable Stochastic Resonance System for Bearing Fault Detection
    He, Lifang
    Xu, Jiaqi
    Huang, Xiaoxiao
    FLUCTUATION AND NOISE LETTERS, 2024,
  • [48] Weak Feature Extraction of Local Gear Damage Based on Underdamped Asymmetric Periodic Potential Stochastic Resonance
    Hu, Bingbing
    Zhang, Shuai
    Peng, Ming
    Liu, Jie
    Liu, Shanhui
    Zhang, Chunlin
    SYMMETRY-BASEL, 2021, 13 (11):
  • [49] Tri-stable stochastic resonance based research on zpw-2000 frequency shift signal detection methods
    Wu, Xiaochun
    Liu, Xinran
    Journal of Railway Science and Engineering, 2024, 21 (08) : 3394 - 3405
  • [50] Optimizing DSFH communication system performance via multi-feedback unsaturated tri-stable stochastic resonance for enhancement of periodic signal
    He, Lifang
    Xiong, Qing
    Bi, Lujie
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2024, 650