自构建关联噪声下的随机共振及其在故障诊断上的应用

徐海涛 1,2, 杨涛 1,2,周生喜 1,2

振动与冲击 ›› 2024, Vol. 43 ›› Issue (11) : 297-305.

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (11) : 297-305.
论文

自构建关联噪声下的随机共振及其在故障诊断上的应用

  • 徐海涛 1,2, 杨涛 1,2,周生喜 1,2
作者信息 +

Stochastic resonance driven by self-constructingly correlated noise and its application in fault diagnosis

  • XU Haitao1,2, YANG Tao1,2, ZHOU Shengxi1,2
Author information +
文章历史 +

摘要

轴承作为旋转机械的重要组件之一,及时对其进行健康监测与更换可有效避免设备停机,减少经济损失。本文首先基于自构建关联噪声驱动下的随机共振系统(Stochastic Resonance System Driven by Self-constructingly Correlated Noise, DSCSR),推导了在正弦激励下该系统输出的理论信噪比(signal-to-noise ratio, SNR)。研究发现通过调节此非线性系统的参数可观察到随机共振现象。其次,针对将随机共振现象用于故障诊断时需要准确的先验知识这一局限性,本文进一步提出了基于功率谱的信噪比评价指标,并以此来确定非线性系统随机共振发生时的最优系统参数,对最优参数系统输出信号进行功率谱分析来判断故障类型。最后,通过轴承故障诊断实验以及实际风机轴承内圈故障实例证明了DSCSR方法的有效性,以及其增强微弱故障特征并抑制其他谐波以及噪声的干扰的能力。

Abstract

Rolling element bearings are the crucial component of rotating machine, timely health monitoring can effectively avoid the breakdown of the machine, further reduce the loss of the economic. Firstly, this paper proposed a stochastic resonance system driven by self-constructingly correlated noise (DSCSR), and theoretically analyzed the signal-to-noise ratio (SNR), which examines that the stochastic resonance can occur by adjusting the systems parameters. Secondly, aiming at the drawback that the accurate prior knowledge should be obtained before analysis, the paper suggested to calculate the SNR based on the power spectrum (〖SNR〗_P). Based on 〖SNR〗_P, the optimized system parameters can be achieved. Therefore, the fault type can be determined according to the output signal of the optimized system. Finally, the bearing fault diagnosis experiment and the bearing inner race fault of an actual wind turbine validate the capability of DSCSR in enhancing the weak fault characteristic and in suppressing the interference of other harmonics or random noise.

关键词

关联噪声 / 随机共振 / 故障诊断 / 非线性系统

Key words

Correlated noise / stochastic resonance / fault diagnosis / nonlinear system

引用本文

导出引用
徐海涛 1,2, 杨涛 1,2,周生喜 1,2. 自构建关联噪声下的随机共振及其在故障诊断上的应用[J]. 振动与冲击, 2024, 43(11): 297-305
XU Haitao1,2, YANG Tao1,2, ZHOU Shengxi1,2. Stochastic resonance driven by self-constructingly correlated noise and its application in fault diagnosis[J]. Journal of Vibration and Shock, 2024, 43(11): 297-305

参考文献

[1] 陈礼顺, 张晗, 陈雪峰, 等. 基于低秩稀疏分解算法的航空锥齿轮故障诊断[J]. 振动与冲击, 2020, 39(12): 103-112. CHEN Lishun, ZHANG Han, CHEN Xuefeng, et al. Fault diagnosis of aero-engine bevel gear based on a low rank sparse model. Journal of vibration and shock, 2020, 39(12): 103-112. [2] Sawalhi N, Randall R B, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2616-2633. [3] MIAO Y, ZHAO M, LIN J, et al. Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings[J], Measurement Science and Technology, 2016, 27(10), 105004. [4] MIAO Y, ZHANG B, LIN J, et al. A review on the application of blind deconvolution in machinery fault diagnosis[J], Mechanical Systems and Signal Processing, 2021, 163:108202. [5] Colominas M A, Schlotthauer G, Torres M E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing[J]. Biomedical Signal Processing and Control, 2014, 14: 19-29. [6] Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE transactions on signal processing, 2013, 62(3): 531-544. [7] Benzi R, Sutera A, Vulpiani A. The mechanism of stochastic resonance[J]. Journal of Physics A: mathematical and general, 1981, 14(11): L453. [8] Benzi R, Parisi G, Sutera A, et al. Stochastic resonance in climatic change[J]. Tellus, 1982, 34(1): 10-16. [9] RYU C,KONG S G,KIM H. Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance[J]. Pattern Recognition Letters, 2011, 32(2): 107-113. [10] MCINNES C R,GORMAN D G,CARTMELL M P. Enhanced vibrational energy harvesting using nonlinear stochastic resonance[J]. Journal of Sound and Vibration, 2008, 318(4-5):655-662. [11] LAI Z, LIU J, ZHANG H, et al. Multi-parameter-adjusting stochastic resonance in a standard tri-stable system and its application in incipient fault diagnosis[J], Nonlinear Dynamics, 2019, 96: 2069-2085. [12] 黄大文, 杨建华, 唐超权, 等. 二阶系统普通变尺度随机共振及轴承故障诊断[J]. 振动、测试与诊断, 2018, 38(6): 1260-1266. HUANG Da-wen, YANG Jian-hua, TANG Chao-quan, et al. General Scale Transformation Stochastic Resonance of the Second-Order System and Bearing Fault Diagnosis[J]. Journal of Vibration, Measurement and Diagnosis, 2018, 38(6): 1260-1266. [13] HUANG D, YANG J, ZHOU D, et al. Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index[J], IEEE Transactions on Instrumentation and Measurement, 2020, 69(1): 109-121. [14] WANG J, HE Q, KONG F. Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings[J]. IEEE Transactions on instrumentation and measurement, 2014, 64(2): 564-577. [15] ZHANG X, MIAO Q, LIU Z, et al. An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis[J], ISA transactions, 2017, 71: 206-214. [16] 陆思良, 苏云升, 赵吉文, 等. 基于二维互补随机共振的轴承故障诊断方法研究[J]. 振动与冲击, 2018, 37(4): 7-12. LU Si-liang, SU Yun-sheng, ZHAO Ji-wen, et al. Bearings fault diagnosis based on two-dimensional complementary stochastic resonance[J]. Journal of vibration and shock, 2018, 37(4): 7-12. [17] LI J, ZHANG J, LI M, 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. [18] 谯自健, 束学道. 非对称势诱导随机共振增强机械重复瞬态提取[J]. 机械工程学报, 2021, 57(23): 160-168. QIAO Zi-jian, SHU Xue-dao. Stochastic Resonance Induced by Asymmetric Potentials Enhanced Mechanical Repetitive Transient Extraction[J]. Journal of Mechanical Engineering, 2021, 57(23): 160-168. [19] LIU J, QIAO Z, DING X, et al. Stochastic resonance induced weak signal enhancement over controllable potential-well asymmetry[J], Chaos Solitons and Fractals, 2021, 146(17): 110845. [20] 贺利芳, 刘秋玲, 张刚. 高斯势分段双稳随机共振在不同噪声下的轴承故障诊断[J]. 振动与冲击, 2023,42(3): 30-42. HE Li-fang, LIU Qiu-ling, ZHANG Gang. Bearing fault diagnosis under different noises with GPPBSR system[J]. Journal of vibration and shock, 2023,42(3): 30-42. [21] 宫涛, 杨建华, 单振, 等. 强噪声背景与变转速工况条件下滚动轴承故障诊断研究[J].工矿自动化, 2021, 47(7): 63-71. GONG Tao, YANG Jian-hua, SHAN Zhen, et al. Research on rolling bearing fault diagnosis under strong noise background and variable speed working condition[J]. Industry and Mine Automation, 2021, 47(7): 63-71. [22] XU H, ZHOU S, YANG T. Stochastic resonance of a high-order-degradation bistable system and its application in fault diagnosis with variable speed condition[J]. Mechanical Systems and Signal Processing, 2023, 186: 109852. [23] QIAO Z, LEI Y, LI N. Applications of stochastic resonance to machinery fault detection: A review and tutorial[J]. Mechanical Systems and Signal Processing, 2019, 122: 502-536. [24] LU S, HE Q, WANG J. A review of stochastic resonance in rotating machine fault detection[J]. Mechanical Systems and Signal Processing, 2018, 116: 230-260. [25] QIAO Z, LEI Y, LIN J, et al. Stochastic resonance subject to multiplicative and additive noise: The influence of potential asymmetries[J], Physical Review E, 2016, 94(5): 052214. [26] JIN Y, XIE W, LIU K. Noise-induced Resonances in a Periodic Potential Driven by Correlated Noises[J], Procedia IUTAM, 2017, 22: 267-274. [27] SHI P, LI Q, HAN D. Stochastic resonance and MFPT in an asymmetric bistable system driven by correlated multiplicative colored noise and additive white noise[J], International Journal of Modern Physics B, 2017, 31(14): 1750113. [28] SHI P, XIA H, HAN D, et al. Stochastic resonance in a time polo-delayed asymmetry bistable system driven by multiplicative white noise and additive color noise[J], Chaos, Solitons & Fractals, 2018, 108: 8-14. [29] XU P, JIN Y. Mean first-passage time in a delayed tristable system driven by correlated multiplicative and additive white noises[J], Chaos Solitons and Fractals, 2018, 112: 75-82. [30] XU P, JIN Y. Stochastic resonance in an asymmetric tristable system driven by correlated noises[J], Applied Mathematical Modelling, 2020, 77: 408-425. [31] ZHANG G, YUN D, ZHANG T. Stochastic Resonance in Unsaturated Piecewise Nonlinear Bistable System Under Multiplicative and Additive Noise for Bearing Fault Diagnosis[J], IEEE Access, 2019, 7: 58435-58448. [32] GUO F, CHENG X, WANG S, et al. Behavior of stochastic resonance for an underdamped bistable system driven by multiplicative and additive signals[J], Physica Scripta, 2021, 96(1): 015001. [33] GUO F, ZHU C, WANG S, et al. Phenomenon of stochastic resonance for an underdamped monostable system with multiplicative and additive noise[J], Indian Journal of Physics, 2022, 96(2): 515-523. [34] JIN Y, WANG H, XU P, et al. Stochastic resonance of a multi-stable system and its application in bearing fault diagnosis[J]. Probabilistic Engineering Mechanics, 2023, 72: 103418. [35] Saidi L, Ali J B, Bechhoefer E, et al. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR[J]. Applied Acoustics, 2017, 120: 1-8. [36] Elforjani M, Shanbr S, Bechhoefer E. Detection of faulty high speed wind turbine bearing using signal intensity estimator technique[J]. Wind Energy, 2018, 21(1): 53-69.

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