本文選題：故障診斷　切入點：模擬電路　出處：《南京郵電大學》2015年碩士論文

【摘要】：模擬電路故障診斷對電路設(shè)計、設(shè)備生產(chǎn)和儀器維護是至關(guān)重要的,故障診斷技術(shù)是目前專家學者們和測試工程師在電路與系統(tǒng)領(lǐng)域中具有挑戰(zhàn)的重要課題。但是由于模擬電路元件參數(shù)的容差性、電路響應(yīng)的連續(xù)性和非連續(xù)性以及環(huán)境因素造成電路故障的多樣性和復雜性,使得傳統(tǒng)的診斷方法無法更好地運用在實際模擬電路故障診斷中。本文基于現(xiàn)代診斷技術(shù),構(gòu)建以提升小波和支持向量機相結(jié)合的模擬電路故障診斷框架,深入研究了故障特征提取和分類器構(gòu)建這兩個模擬電路故障診斷中的重要環(huán)節(jié)。本文取得成果如下:(1)在總結(jié)已有的模擬電路故障特征提取方法基礎(chǔ)之上,本文提出了將提升db5小波運用到電路故障特征提取當中。這是因為db5小波函數(shù)與故障輸出信號具有較高的相似度,有利于提取信號的重要特征,而且分解速度快。與非提升方法相比能夠更加準確地反映原始信號的特征。通過仿真實例得到的數(shù)據(jù)驗證了該方法的優(yōu)越性。(2)針對目前模擬電路故障識別中遇到的難題,本文構(gòu)建了基于馬氏距離的最小二乘支持向量機分類器。該方法通過運用最小二乘把復雜的求解問題簡單化了,通過引入馬氏距離,改善了最小二乘支持向量機的稀疏性,從而節(jié)省了分類器的訓練時間。仿真實例表明,此方法可以有效地運用在模擬電路故障診斷之中。(3)為了提高支持向量機的泛化學習能力,本文通過粒子群算法優(yōu)化最小二乘支持向量機的結(jié)構(gòu)參數(shù),但是由于粒子群容易陷入局部最優(yōu)和早熟收斂問題,所以對標準PSO算法進行改進,提出了吸引-排斥控制粒子群優(yōu)化方法,通過控制粒子的吸引運動和排斥運動提高粒子的多樣性,避免了粒子在進化過程中陷入了局部最優(yōu)和早熟收斂問題。
[Abstract]:Analog circuit fault diagnosis is very important for circuit design, equipment production and instrument maintenance. Fault diagnosis technology is an important subject for experts and test engineers in the field of circuits and systems. However, because of the tolerance of analog circuit components, The variety and complexity of circuit fault caused by the continuity and discontinuity of circuit response and environmental factors make it impossible for the traditional diagnosis method to be better used in the actual analog circuit fault diagnosis. An analog circuit fault diagnosis framework based on lifting wavelet and support vector machine is constructed. In this paper, the fault feature extraction and classifier construction are studied in detail. The main achievements of this paper are as follows: 1) on the basis of summarizing the existing methods of fault feature extraction in analog circuits, In this paper, the lifting db5 wavelet is applied to circuit fault feature extraction, which is due to the high similarity between the db5 wavelet function and the fault output signal, which is helpful to extract the important features of the signal. Moreover, the decomposition speed is fast. Compared with the non-lifting method, it can reflect the characteristics of the original signal more accurately. The data obtained from the simulation example verify the superiority of the method. In this paper, the least squares support vector machine classifier based on Markov distance is constructed. The method simplifies the complex problem by using least square, and improves the sparsity of least squares support vector machine by introducing Markov distance. Thus, the training time of classifier is saved. The simulation example shows that this method can be effectively used in analog circuit fault diagnosis. In this paper, the structure parameters of least squares support vector machine are optimized by particle swarm optimization, but the standard PSO algorithm is improved because the particle swarm is easy to fall into local optimal and premature convergence problems. An attractive repulsive control particle swarm optimization method is proposed to improve the diversity of particles by controlling the attractive and repellent motion of particles and to avoid the problem of local optimum and premature convergence of particles in the evolution process.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN710

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