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| 二次采样随机共振的工程应用研究 | |
| 引用本文： | 冷永刚,王太勇,郭焱,许俊艳.二次采样随机共振的工程应用研究[J].中国机械工程,2004,15(20):1847-1852. |
| 作者姓名： | 冷永刚 王太勇 郭焱 许俊艳 |
| 作者单位： | 1. 天津大学机械工程学院,天津,300072;山东工商学院信息与电子工程学院,烟台,264005 2. 天津大学机械工程学院,天津,300072 3. 山东工商学院管理科学与工程学院,烟台,264005 |
| 基金项目： | 国家自然科学基金资助项目 ( 5 0 175 0 81)，国家 863高技术研究发展计划资助项目 ( 2 0 0 2AA414 42 0 )，振动、冲击、噪声国家重点实验室基金资助项目 (VSN -2 0 0 4-0 5 ) |
| 摘 要： | 研究了小参数随机共振(SR)理论的克莱默斯(Kramers)逃逸速率和罗伦兹(Lorentzian)分布的噪声频谱特性，得出了只有在噪声能量集中的低频区域才能产生随机共振的论点。在此基础上，通过二次采样频率变换思想，成功地将小参数随机共振扩展应用到大参数的随机共振，从而实现了从强噪声中检测出弱信号的目的。大参数随机共振谱特性及其信噪比的进一步定量分析表明，提高采样频率可把弱信号移入噪声能量集中的低频区域，有利于产生可辨识的随机共振谱峰。电机故障的监测与诊断实例证明了该方法在实际应用中的有效性。 |
| 关 键 词： | 二次采样随机共振 噪声 频谱 弱信号检测 |
| 文章编号： | 1004-132Ⅹ(2004)20-1847-06 |
Engineering Application Study of Twice Sampling Stochastic Resonance |
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| Leng Yonggang , Wang Taiyong Guo Yan Xu Junyan .Tianjin University,Tianjin, .Shandong Institute of Business and Technology,Yantai.Engineering Application Study of Twice Sampling Stochastic Resonance[J].China Mechanical Engineering,2004,15(20):1847-1852. | |
| Authors: | Leng Yonggang Wang Taiyong Guo Yan Xu Junyan Tianjin University Tianjin Shandong Institute of Business and Technology Yantai |
| Affiliation: | Leng Yonggang 1,2 Wang Taiyong 1 Guo Yan 2 Xu Junyan 2 1.Tianjin University,Tianjin,300072 2.Shandong Institute of Business and Technology,Yantai,264005 |
| Abstract: | The Kramers escape rate and the characteristics of noise frequency spectrum distributing as Lorentzian form were investigated under the theory of small parameter stochastic resonance (SR), and the viewpoint of creating stochastic resonance only in the low frequency region where the noise energy has been concentrated was also obtained. On the basis of the investigation and through the methodology of twice sampling frequency transformation, the small parameter stochastic resonance is successfully expanded into the applications of the large parameter stochastic resonance, and hence a weak signal submerged in strong noise is detected. The further quantitative analysis of the power spectral characteristics of the large parameter stochastic resonance and its signal-to-noise ratio indicate that the increase of sampling frequency can move the weak signal into the low frequency region concentrated by noise energy, which is benefit to produce a distinguishable SR spectral peak. The real example of monitoring and diagnosis of electromotor faults proves the validity of the technique in practical applications. |
| Keywords: | twice sampling stochastic resonance noise frequency spectrum weak signal detection |
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