工业工程

工业工程 ›› 2023, Vol. 26 ›› Issue (1): 136-145.doi: 10.3969/j.issn.1007-7375.2023.01.016

• 系统建模与优化算法 • 上一篇    下一篇

CS自适应随机共振与ITD相结合的轴承弱故障诊断

权振亚1,2, 张学良1, 李鹏程3   

  1. 1. 太原科技大学 机械工程学院，山西 太原 030024;
    2. 山西水利职业技术学院 机电工程系，山西 太原 030032;
    3. 北京博华信智科技股份有限公司，北京 100000
  • 收稿日期:2022-01-23 发布日期:2023-03-09
  • 作者简介:权振亚(1980—)，女，山西省人，博士研究生，主要研究方向为旋转机械故障诊断
  • 基金资助:
    山西省研究生教育创新资助项目 (2018BY105)

Weak Fault Diagnosis of Bearing Based on Cuckoo Adaptive Stochastic Resonance and ITD

QUAN Zhenya1,2, ZHANG Xueliang1, LI Pengcheng3   

  1. 1. School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;
    2. Department of Mechanical and Electrical Engineering, Shanxi Conservancy Technical Institute, Taiyuan 030032, China;
    3. Beijing Bohua Xinzhi Technology, Beijing 100000, China
  • Received:2022-01-23 Published:2023-03-09

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摘要： 为了解决轴承早期弱故障诊断的问题，提出将固有时间尺度分解 (intrinsic time-scale decomposition，ITD) 与布谷鸟自适应随机共振 (cuckoo adaptive stochastic resonance，CASR) 相结合的方法进行滚动轴承早期弱故障特征频率提取。针对采集到的滚动轴承振动信号复杂且信噪比 (SNR) 低、故障特征难以提取的问题，结合ITD能抑制端点效应、运算复杂度低等优势，该方法将信噪比作为随机共振的目标函数，通过仿真信号分析及实例验证，将ITD作为CASR处理信号的前处理，使滚动轴承故障信号显著加强，信噪比提高2.17倍，故障特征得到有效提取。

关键词: 滚动轴承, 固有时间尺度分解, 弱故障, 随机共振

Abstract: In order to solve the problem of early weak fault diagnosis of bearings, the intrinsic time-scale decomposition (ITD) is proposed. ITD and cuckoo adaptive stochastic resonance (CASR) are combined to extract feature frequencies of early weak faults of rolling bearings. Rolling bearing vibration signals were collected for complex and low signal-to-noise ratio (SNR) and difficult problem to extract the fault features, the combination of ITD can inhibit the endpoint effects and low computation complexity, this method will take signal-to-noise ratio as the objective function of the stochastic resonance, through the simulation signal analysis and examples, will adopt ITD as pretreatment of CASR signals processing. The signal-to-noise ratio can be increased by 2.17 times, and the fault features can be effectively extracted.

Key words: rolling bearing, intrinsic time-scale decomposition, weak fault, stochastic resonance

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