[1]葛江华,王岩,王亚萍,等. 基于QGA和随机共振的轴承微弱故障信号检测方法[J].哈尔滨理工大学学报,2020,25(03):94-101.[doi:10.15938/j.jhust.2020.03.015]
 GE Jiang hua,WANG Yan,WANG Ya ping,et al. A Weak Signal Detection Method for Bearing Based on QGA and Stochastic Resonance[J].哈尔滨理工大学学报,2020,25(03):94-101.[doi:10.15938/j.jhust.2020.03.015]
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 基于QGA和随机共振的轴承微弱故障信号检测方法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
25
期数:
2020年03期
页码:
94-101
栏目:
机械动力工程
出版日期:
2020-06-25

文章信息/Info

Title:
 
A Weak Signal Detection Method for Bearing Based on QGA and Stochastic Resonance
文章编号:
1007-2683(2020)03-0094-08
作者:
 葛江华王岩王亚萍孙永国许迪
 哈尔滨理工大学 机械动力工程学院,哈尔滨 150080)
Author(s):
 GE JianghuaWANG YanWANG YapingSUN YongguoXu Di
 
(School of Mechanical and Dynamic Engineering, Harbin University of Science and Technology, Harbin 150080, China)
关键词:
 关键词:早期故障诊断微弱信号检测量子遗传算法随机共振
Keywords:
 Keywords:early fault diagnosis weak signal detection quantum genetic algorithm stochastic resonance
分类号:
TH165
DOI:
10.15938/j.jhust.2020.03.015
文献标志码:
A
摘要:
 
摘要:针对滚动轴承早期故障阶段振动信号微弱,信噪比低,提出量子遗传算法(quantum genetic algorithm,简称QGA)与随机共振相结合的微弱信号检测方法,提高信号信噪比并识别故障位置。首先,对大参数信号变尺度处理,并根据输入信号对噪声强度进行估计实现参数初始化;其次,以输出信噪比作为目标函数,通过QGA对系统的双参数进行自适应寻优;最后,通过系统的随机共振实现微弱信号信噪比的提高。仿真及实验结果表明,该方法充分考虑了系统参数之间的相互作用,能够有效提高信号信噪比,实现了早期故障阶段的微弱信号检测。
Abstract:
 Abstract:Aiming at the problems that the vibration signal is weak and the SNR is low in its early failure stage of rolling bearing, a weak signal detection method combining Quantum Genetic Algorithm (QGA) and Stochastic Resonance is proposed, which improves SNR and identifies fault location. Firstly, the large parameter signal is scale transformed and the noise intensity is estimated according to the input signal to realize the initialization of the parameters. Secondly, the output SNR is selected as the objective function, and the two parameter are dealt with adaptive optimization through the QGA; Finally, the SNR of weak signal is improved by stochastic resonance system. Simulation and experimental results show that the method fully considers the interaction between system parameters, and can effectively improve SNR, and achieve early detection of weak signal in failure stage.

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期: 2018-08-27
基金项目:
国家自然科学基金(51575143);黑龙江省自然科学基金(E2018046).
作者简介:
葛江华(1963—),女,博士,教授;
王岩(1992—),男,硕士研究生.
通信作者:
王亚萍(1972—)女,博士,教授,硕士研究生导师,Email:wypbl@163.com.
更新日期/Last Update: 2020-10-14