随机共振
非周期图
噪音(视频)
梯度噪声
信号(编程语言)
稳健性(进化)
控制理论(社会学)
背景噪声
数值噪声
达芬方程
数学
转化(遗传学)
计算机科学
噪声测量
算法
降噪
噪声地板
物理
人工智能
非线性系统
电信
生物化学
化学
控制(管理)
组合数学
图像(数学)
基因
程序设计语言
量子力学
作者
Zhen Shan,Zhong-Qiu Wang,Jianhua Yang,Dengji Zhou,Houguang Liu
标识
DOI:10.1177/10775463221109715
摘要
The extraction of non-stationary feature information under strong noise background is a difficult problem. In this paper, a novel general time-varying scale transformation aperiodic stochastic resonance is proposed to extract and enhance the weak non-stationary signal under strong noise background. The theoretical framework of a parameters time-varying Duffing system is built for aperiodic stochastic resonance. By studying the resonance region migration when scale coefficient takes different values, an optimal scale transformation is achieved. Also, the time-varying system is optimized with cross-correlation coefficient as the index. Compared with the existing methods, the proposed method can be applied to stronger noise background and has stronger noise robustness. When under the same noise background, the proposed method can provide output with higher signal-to-noise ratio and higher cross-correlation coefficient. Finally, experimental analysis of faulty bearing vibration signal verifies the high accuracy, which indicates a good signal extraction and enhancement ability of the proposed method.
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