采用形变周期势系统的轴承故障诊断方法

徐浩; 张刚; 张天骐

doi:10.7652/xjtuxb202008010

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采用形变周期势系统的轴承故障诊断方法

更新时间：2025-07-09

- 采用形变周期势系统的轴承故障诊断方法
- A Bearing Fault Diagnosis Method Using Deformable Periodic Potential System
- 西安交通大学学报 2020年54卷第8期页码：77-83
- 作者机构：
  
  重庆邮电大学通信与信息工程学院,重庆,400065
- 作者简介：
- 基金信息：
  
  : 2020-02-17。作者简介: 徐浩(1996—),男,硕士生;张刚(通信作者),男,教授。基金项目: 国家自然科学基金资助项目(61771085,61371164);重庆市教委科研基金资助项目(KJQN201900601)
- DOI：10.7652/xjtuxb202008010
  中图分类号： TH133.33
- 网络首发：2020-08-10，
  
  纸质出版：2020
- 稿件说明：
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徐浩, 张刚, 张天骐. 采用形变周期势系统的轴承故障诊断方法[J]. 西安交通大学学报, 2020,54(8):77-83.

A Bearing Fault Diagnosis Method Using Deformable Periodic Potential System[J]. 2020, 54(8): 77-83.
徐浩, 张刚, 张天骐. 采用形变周期势系统的轴承故障诊断方法[J]. 西安交通大学学报, 2020,54(8):77-83. DOI： 10.7652/xjtuxb202008010.

A Bearing Fault Diagnosis Method Using Deformable Periodic Potential System[J]. 2020, 54(8): 77-83. DOI： 10.7652/xjtuxb202008010.

摘要

针对在高强度噪声环境下的轴承故障信号难以检测的问题

提出一种利用形变周期势系统(DPPS)的轴承故障诊断方法。该方法首先将掺杂噪声的故障信号输入DPPS中

组成以DPPS为核心的随机共振(SR)系统; 然后

以功率谱放大倍数(SPA)和幅度响应为测度指标来量化DPPS轴承故障诊断方法对轴承故障特征信号的增强效果

通过矩量法和概率流方法推导SPA和幅度响应的解析式

得到当SPA和幅度响应最大时的DPPS诊断方法的最优设置参数; 最后

在相同条件下

将该诊断方法应用于轴承内外圈故障诊断

并与新型幂指三稳势系统(NCETS)轴承故障诊断方法作对比实验。实验结果表明

DPPS轴承故障诊断方法能够利用噪声的能量分别将内外圈故障特征频率的功率谱幅值提高至1 950和2 950 W/Hz

从而可以在功率谱中轻易识别

进而断定轴承的内外圈出现了故障

而NCETS故障诊断方法仅能分别提高至359.2和575.6 W/Hz

证明了采用DPPS的轴承故障诊断方法的有效性和先进性。

Abstract

A bearing fault diagnosis method using the deformation periodic potential system(DPPS)is proposed to solve the problem that the bearing fault signal is difficult to detect in a high-intensity noise environment. The method first inputs the noise-doped fault signal into the DPPS to form a stochastic resonance(SR)system with DPPS as the core and to detect the bearing fault characteristic frequency from the environmental noise and judge the bearing fault type. Then

the spectral power amplification(SPA)and amplitude response are used as measurement indicators to quantify the enhancement effect of the method on the bearing fault characteristic signal. Analytical formulas of SPA and amplitude response are derived using the method of moments and the probability flow method

and the optimal setting parameters of the DPPS diagnosis method are obtained when SPA and amplitude response are maximum. Finally

the DPPS diagnosis method is applied to the fault diagnosis of bearing inner and outer ring

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references

BENZI R, PARISI G, SUTERA A, et al. A theory of stochastic resonance in climate change [J]. SIAM Journal on Applied Mathematics, 1983, 43(3): 565-578.

AMIRPASHA Z, NIKITA N, BORIS G. Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit [J]. Communications in Nonlinear Science and Numerical Simulation, 2020, 82: 105024.

SINGH M, VERMA A, SHARMA N. An optimized cascaded stochastic resonance for the enhancement of brain MRI [J]. IRBM, 2018, 39: 334-342.

ZHANG Lu, LAI Li, PENG Hao, et al. Stochastic and superharmonic stochastic resonances of a confined overdamped harmonic oscillator [J]. Physical Review E, 2018, 97(1): 012147.

XIE Min, FAN Bixuan, HE Xiaoli, et al. Interference effect in optomechanical stochastic resonance [J]. Physical Review: E, 2018, 98(5): 052202.

SHAO Zhengzheng, YIN Zhizhen, SONG Helun, et al. Fast detection of a weak signal by a stochastic resonance induced by a coherence resonance in an excitable GaAs/Al0.45Ga0.55 as superlattice [J]. Physical Review Letters, 2018, 121(8): 086806.

ISHANT T, RICHA P, PARMANANDA P, et al. Intrinsic periodic and aperiodic stochastic resonance in an electrochemical cell [J]. Physical Review: E, 2016, 94(2): 022210.

LU Siliang, HE Qingbo, WANG Jun. A review of stochastic resonance in rotating machine fault detection [J]. Mechanical Systems and Signal Processing, 2019, 116: 230-260.

QIAO Zijian, LEI Yaguo, LI Naipeng. Applications of stochastic resonance to machinery fault detection: a review and tutorial [J]. Mechanical Systems and Signal Processing, 2019, 122: 502-536.

LI Jimeng, ZHANG Jinfeng, LI Ming, et al. A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis [J]. Mechanical Systems and Signal Processing, 2019, 114: 128-145.

谢勇, 刘若男. 过阻尼搓板势系统的随机共振 [J]. 物理学报, 2017, 66(12): 87-96.

XIE Yong, LIU Ruonan. Stochastic resonance of an overdamped washboard potential system [J]. Acta Physica Sinica, 2017, 66(12): 87-96.

王慧, 张刚, 张天骐. 改进型双稳随机共振系统及其在轴承故障诊断的应用 [J]. 西安交通大学学报, 2020, 4(54): 110-117.

WANG Hui, ZHANG Gang, ZHANG Tianqi. Improved bistable stochastic resonance system and its application in bearing fault diagnosis [J]. Journal of Xi'an Jiaotong University, 2020, 4(54): 110-117.

NICOLIS C. Stochastic resonance in multistable systems: the role of intermediate states [J]. Physical Review: E, 2010, 82: 011139.

胡岗. 随机力与非线性系统 [M]. 上海: 上海科技教育出版社, 1994.

REENBOHN W L, MAHATO M C. Dynamical states, stochastic resonance, and ratchet effect in a biharmonically driven sinusoidal potential [J]. Physical Review: E, 2015, 91(5): 052151.

ASISH K D. Signal amplification factor in stochastic resonance: an analytic non-perturbative approach [J]. Physica: D Nonlinear Phenomena, 2015, 303: 1-17.

张刚, 高俊鹏. 组合型幂指函数三稳态随机共振微弱信号检测 [J]. 计算机应用, 2018, 38(9): 2747-2752.

ZHANG Gang, GAO Junpeng. Weak signal detection based on combination of power and exponential function model in tri-stable stochastic resonance [J]. Journal of Computer Applications, 2018, 38(9): 2747-2752.

邹创, 陶涛, 姜歌东, 等. 考虑力学特性的谐波齿轮啮合参数优化方法 [J]. 西安交通大学学报, 2019, 53(2): 16-23.

ZOU Chuang, TAO Tao, JIANG Gedong, et al. A method to optimize the meshing parameters of harmonic gears considering mechanical properties [J]. Journal of Xi'an Jiaotong University, 2019, 53(2): 16-23.

CWRU. 12k drive end bearing fault data [DB/OL]. [2019-05-15]. http: ∥csegroups.case.edu/bearingdatacenter/pages/download-data-file.

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