本文選題：隨機(jī)共振　切入點(diǎn)：參數(shù)優(yōu)化　出處：《中國計(jì)量學(xué)院》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：隨機(jī)共振是以噪聲為媒介引起微弱周期信號(hào)與非線性系統(tǒng)協(xié)同作用的非線性現(xiàn)象，涉及的參數(shù)有周期信號(hào)的幅值、頻率，噪聲強(qiáng)度和非線性系統(tǒng)參數(shù)。在實(shí)際應(yīng)用中，輸入信號(hào)和噪聲是給定的，只有通過調(diào)節(jié)非線性系統(tǒng)參數(shù)，使非線性系統(tǒng)與輸入信號(hào)匹配，才能產(chǎn)生隨機(jī)共振。本文分析了雙穩(wěn)系統(tǒng)參數(shù)對(duì)隨機(jī)共振的影響，提出基于人工魚群算法的自適應(yīng)隨機(jī)共振。 分析了雙穩(wěn)系統(tǒng)參數(shù)對(duì)勢壘高度的影響以及系統(tǒng)輸出信噪比隨雙穩(wěn)系統(tǒng)參數(shù)的變化，通過調(diào)節(jié)雙穩(wěn)系統(tǒng)參數(shù)實(shí)現(xiàn)了隨機(jī)共振的產(chǎn)生與增強(qiáng)。 研究了常用自適應(yīng)算法的特點(diǎn)，針對(duì)線性隨機(jī)搜索算法采用疊加權(quán)值的方法，無法保證全局最優(yōu)解和遺傳算法因?yàn)橐腚S機(jī)突變而搜索到錯(cuò)誤空間的不足，提出了基于人工魚群算法的自適應(yīng)隨機(jī)共振，利用人工魚群算法自適應(yīng)地調(diào)節(jié)雙穩(wěn)系統(tǒng)參數(shù)，實(shí)現(xiàn)隨機(jī)共振；將兩個(gè)雙穩(wěn)系統(tǒng)經(jīng)過非線性耦合的方式構(gòu)成耦合系統(tǒng)，通過耦合的作用控制隨機(jī)共振的產(chǎn)生，進(jìn)而對(duì)控制參數(shù)的優(yōu)化增強(qiáng)共振效應(yīng)。 將基于人工魚群算法的自適應(yīng)隨機(jī)共振應(yīng)用于軸承滾動(dòng)體故障、內(nèi)圈故障的檢測和不同流量的渦街信號(hào)的檢測，成功地獲取了故障特征頻率和渦街頻率。實(shí)驗(yàn)結(jié)果表明，利用人工魚群算法并行優(yōu)化雙穩(wěn)系統(tǒng)參數(shù)，能夠增強(qiáng)微弱的特征信號(hào)，提高信噪比，有效地實(shí)現(xiàn)微弱信號(hào)的檢測。 最后，利用COM技術(shù)的LabVIEW與MATLAB的無縫集成，開發(fā)了微弱信號(hào)智能檢測系統(tǒng)，該系統(tǒng)能夠根據(jù)不同的被測信號(hào)特性，自適應(yīng)地調(diào)節(jié)雙穩(wěn)系統(tǒng)參數(shù)，，實(shí)現(xiàn)隨機(jī)共振。經(jīng)對(duì)渦街信號(hào)的檢測表明系統(tǒng)能有效地實(shí)現(xiàn)微弱特征信號(hào)的檢測，具有廣闊的應(yīng)用前景。
[Abstract]:Stochastic resonance (SR) is a nonlinear phenomenon in which the weak periodic signal and the nonlinear system interact with each other by using noise as the medium. The parameters involved include amplitude, frequency, noise intensity and nonlinear system parameters of the periodic signal. The input signal and noise are given. Only by adjusting the parameters of the nonlinear system, can the nonlinear system match with the input signal to produce stochastic resonance. In this paper, the influence of the bistable system parameters on the stochastic resonance is analyzed. Adaptive stochastic resonance based on artificial fish swarm algorithm is proposed. The influence of bistable system parameters on the barrier height and the variation of output SNR with bistable system parameters are analyzed. The stochastic resonance is generated and enhanced by adjusting the bistable system parameters. In this paper, the characteristics of common adaptive algorithms are studied. The method of superposition weights is used in linear random search algorithm, which can not guarantee the global optimal solution and the deficiency of genetic algorithm to search the wrong space because of the introduction of random mutation. Adaptive stochastic resonance based on artificial fish swarm algorithm is proposed. The parameters of bistable system are adjusted adaptively by artificial fish swarm algorithm to realize stochastic resonance. The stochastic resonance is controlled by coupling, and the resonance effect is enhanced by optimizing the control parameters. Adaptive stochastic resonance based on artificial fish swarm algorithm is applied to the detection of bearing rolling body fault, inner ring fault and vortex signal with different flow rate. The fault characteristic frequency and vortex frequency are obtained successfully. The experimental results show that, By using artificial fish swarm algorithm to optimize the parameters of bistable system in parallel, the weak characteristic signal can be enhanced, the signal-to-noise ratio (SNR) can be improved, and the weak signal can be detected effectively. Finally, using the seamless integration of LabVIEW and MATLAB of COM technology, a weak signal intelligent detection system is developed. The system can adjust the parameters of bistable system adaptively according to different characteristics of measured signal. The detection of vortex signal shows that the system can detect the weak characteristic signal effectively and has a broad application prospect.
【學(xué)位授予單位】：中國計(jì)量學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TB53;TP18

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