随机共振
断层(地质)
概率密度函数
非线性系统
控制理论(社会学)
噪音(视频)
反褶积
蒙特卡罗方法
信号(编程语言)
随机过程
计算机科学
振动
算法
工程类
数学
声学
物理
统计
人工智能
地质学
地震学
图像(数学)
量子力学
程序设计语言
控制(管理)
作者
Haitao Xu,Shengxi Zhou
出处
期刊:Measurement
[Elsevier]
日期:2024-02-01
卷期号:225: 114018-114018
被引量:1
标识
DOI:10.1016/j.measurement.2023.114018
摘要
This paper analyses the stochastic resonance of a double delay-controlled nonlinear system based on the high-order-degradation potential function. It provides a theoretical derivation of the stationary probability density, mean first-passage time (MFPT), and signal-to-noise ratio (SNR), while the marginal probability density is examined via Monte-Carlo simulation. In the field of fault detection for faulty bearings, the stochastic resonance of the nonlinear system encounters a challenge where the exact fault period must be known beforehand. Unfortunately, it is not available for the real engineering application. Consequently, this paper proposes the periodicity-assisted stochastic resonance method, which uses the blind deconvolution technique. The proposed method allows for adaptive determination of the fault frequency from the vibration signal and successfully enhances the power of the signal component corresponding to the determined fault frequency. Compared with the state-of-the-art methods, the proposed method shows better performance, which is validated by the simulated and experimental data. Overall, the proposed method is advantageous for enhancing the weak fault characteristic which is interfered by the strong noise and random impulses.
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