Adaptive Frequency-Shifted and Re-Scaling Stochastic Resonance with Applications to Fault Diagnosis

您当前的位置：

首页 >

文章列表页 >

Adaptive Frequency-Shifted and Re-Scaling Stochastic Resonance with Applications to Fault Diagnosis

更新时间：2025-07-09

- Adaptive Frequency-Shifted and Re-Scaling Stochastic Resonance with Applications to Fault Diagnosis
- Vol. 43, Issue 7, Pages: 69-73(2009)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH17
- Online First：10 July 2009，
  
  Published：2009
- 稿件说明：
移动端阅览
Adaptive Frequency-Shifted and Re-Scaling Stochastic Resonance with Applications to Fault Diagnosis[J]. 2009, 43(7): 69-73.
DOI：

Adaptive Frequency-Shifted and Re-Scaling Stochastic Resonance with Applications to Fault Diagnosis[J]. 2009, 43(7): 69-73. DOI：

摘要

针对移频变尺度随机共振系统参数选择困难的问题

提出了一种基于时频指标的自适应移频变尺度随机共振算法.该算法选用了最高谱峰位置(频域)及过零间距方差(时域)作为优化目标函数

避开了以信噪比等为目标函数时需要预先知道目标信号确切频率的不足

能够自适应地获取最优系统参数

以最大信噪比检测出微弱周期信号.同时

由于移频变尺度随机共振的选用

该自适应算法突破了传统随机共振系统的限制

因此可以检测实际工程中的高频信号.仿真和故障诊断的工程应用结果表明

该自适应算法简单、易于理解

能有效地从强背景噪声中检测出高频微弱周期信号

具有较强的工程实用价值.

Abstract

The difficulty for selecting the best system parameters restricts engineering applications to frequency-shifted and re-scaling stochastic resonance(FRSR). An adaptive FRSR method with time-domain and frequency-domain indexes is developed

where the station of the highest spectral peak and the variance of the zero-crossing distance are chosen as the objective functions

and the optimal parameters are obtained adaptively without predicting the exact frequency of the target signal. Once FRSR is involved

the proposed method is able to detect high frequency

while the traditional stochastic resonance is only for low frequency. The simulation and engineering application on fault diagnosis indicate the ability for weak periodic signals buried in heavy noise.

关键词

Keywords

references

BENZI R, SUTERA A, VULPIANA A. The mechanism of stochastic resonance [J]. Journal of Physics A: Mathematical and General, 1981, 14(11):L453-L457.

GAMMAITONI L, HÄNGGI P, JUNG P, et al. Stochastic resonance [J]. Reviews of Modern Physics, 1998, 70(1): 223-287.

杨定新,胡茑庆,温熙森. 双稳系统数值仿真模型的稳定性分析 [J].系统仿真学报,2005,17(10):31-33.

YANG Dingxin, HU Niaoqing, WEN Xisen. Stability analysis for numerical simulation model of bistable system [J]. Acta Simulate Systematica Sinica, 2005, 17(10):31-33.

冷永刚,王太勇,郭焱,等.级联双稳系统的随机共振特性 [J].物理学报,2005,54(3):1118-1125.

LENG Yonggang, WANG Taiyong, GUO Yan, et al. Stochastic resonance behaviors of bistable systems connected in series [J]. Acta Physica Sinica, 2005,54(3):1118-1125.

GORA P F. An exactly solvable nonlinear model: constructive effects of correlations between Gaussian noises [J]. Physica: A, 2007, 378(2):201-210.

XU B, DUAN F, BAO R, et al. Stochastic resonance with tuning system parameters: the application of bistable systems in signal processing [J]. Chaos, Solitons Fractals, 2002, 13(4): 633-644.

MITAIM S, KOSKO B. Adaptive stochastic resonance IEEE Special issue on intelligent signal processing, [J]. 1998, 86(11): 2152-2183.

邓学欣,王太勇,冷永刚,等.自适应扫频随机共振研究[J].西安交通大学学报,2005,39(1):108-110.

DENG Xuexin, WANG Taiyong, LENG Yonggang,et al. Study on adaptive swept stochastic resonance algorithm [J]. Journal of Xi'an Jiaotong University, 2005,39(1):108-110.

TAN Jiyong, CHEN Xuefeng, WANG Junying, et al. Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis [J]. Mechanical Systems and Signal Processing, 2009(23):811-822.

STOCKS N G. Suprathreshold stochastic resonance in multilevel threshold systems [J]. Physical Review Letters, 2000, 84(11):2310-2313.

放大转发异步协作通信系统中的差分空时频传输方案.西安交通大学学报,2009,43(6):62-66.

总体平均经验模式分解与1.5维谱方法的研究.西安交通大学学报,2009,43(5):94-98.

基于小世界原理的模型降阶优化研究.西安交通大学学报,2009,43(1):108-1103.

气动阀间歇性排气噪声辐射规律的研究.西安交通大学学报,2008,42(9):1091-1095.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis

Dual-Wavelet Dictionary Based Feature Extraction Method for Compound Faults in Gearboxes

Study on the Interpretability of One-Dimensional Convolutional Neural Networks in Mechanical Fault Feature Extraction

Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

Application of Interpretable Convolutional Neural Network Enabled by Bi-Damped Wavelets in Bearing Fault Diagnosis

Related Author

LIN Jinshan

LI Yuan

FANG Qixuan

LIU Zhihao

LI Chunpeng

ZHANG Guanjun

WANG Xiangdong

LIU Qiang

Related Institution

西安交通大学电气工程学院，710049，西安School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

College of Hydraulic & Environmental Engineering, China Three Gorges University

China Yangtze Power Co., Ltd.

College of Mechanical and Power Engineering, China Three Gorges University

Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University

⁰