本文選題：模擬電路　切入點：故障診斷　出處：《電子科技大學(xué)》2014年碩士論文

【摘要】：據(jù)資料統(tǒng)計,電子設(shè)備中80%的故障都是由模擬電路導(dǎo)致的。因此,模擬電路故障診斷技術(shù)是電子系統(tǒng)健康管理的重點和難點。另外,隨著電動汽車領(lǐng)域的發(fā)展,鋰離子電池得到了廣泛的應(yīng)用,鋰離子電池的壽命成為保障電動汽車性能和安全的關(guān)鍵。因此,鋰離子電池壽命預(yù)測是電子系統(tǒng)健康管理的另一個重要研究課題�；谏鲜鲈�,本論文主要完成以下的工作:1.模擬電路故障診斷方法的研究。根據(jù)支持向量機進行模式分類時的本質(zhì),提出了采用統(tǒng)計特征量:均值、方差、標準偏差、熵、峭度、偏斜度和形心來組成故障特征向量。同時,針對目前支持向量機進行模式分類時的缺陷,即采用同一個特征向量組合來訓(xùn)練支持向量機所有的二分類器,然而支持向量機的每個二分類器對于不同的特征向量組合有不同的分類精度,提出了基于粒子群算法的特征優(yōu)選方法。實驗結(jié)果表明提出的方法提高了模擬電路故障診斷的精度。2.鋰離子電池壽命預(yù)測方法的研究。根據(jù)鋰離子電池壽命預(yù)測的原理,提出了鋰離子電池壽命預(yù)測的整體框架。首先,本文提出了采用Verhulst模型作為鋰離子電池壽命退化模型。由于傳統(tǒng)的Verhulst模型預(yù)測精度不高,提出了采用粒子群算法對傳統(tǒng)的模型進行優(yōu)化,提高了預(yù)測精度。其次,估計Verhulst模型的參數(shù),本文提出了采用粒子群算法搜索模型的參數(shù)。最后,為了降低噪聲對預(yù)測結(jié)果的影響,采用粒子濾波對模型參數(shù)進行更新。實驗結(jié)果表明提出的方法可以以較小的誤差預(yù)測出鋰離子電池的剩余壽命。3.診斷與預(yù)測系統(tǒng)的軟件設(shè)計。為了滿足電子系統(tǒng)故障診斷與故障預(yù)測的需求,分別設(shè)計了電子系統(tǒng)故障診斷與預(yù)測系統(tǒng)的軟件。診斷系統(tǒng)集成了支持向量機分類算法,對外部讀入的數(shù)據(jù)自動進行故障特征的計算,并迅速給出故障診斷的結(jié)果。預(yù)測系統(tǒng)集成了多個常用的預(yù)測模型,如Verhulst模型、GM(1,1)模型、AR模型,對于外部讀入的數(shù)據(jù)序列,可以自動優(yōu)選出合適的預(yù)測算法,并給出預(yù)測結(jié)果。經(jīng)實驗數(shù)據(jù)驗證,診斷與預(yù)測系統(tǒng)都可以高效地給出精確的結(jié)果。
[Abstract]:According to data statistics, 80% of failures in electronic equipment are caused by analog circuits.Therefore, analog circuit fault diagnosis technology is the focus and difficulty of electronic system health management.In addition, with the development of electric vehicle field, lithium ion battery has been widely used. The life of lithium ion battery has become the key to ensure the performance and safety of electric vehicle.Therefore, Li-ion battery life prediction is another important research topic in electronic system health management.Based on the above reasons, this paper mainly completes the following work: 1.Research on Fault diagnosis of Analog Circuits.According to the nature of pattern classification using support vector machines (SVM), a fault feature vector is proposed, which is composed of statistical features: mean, variance, standard deviation, entropy, kurtosis, skew and centroid.At the same time, aiming at the shortcoming of pattern classification of support vector machine, that is, using the same feature vector combination to train all the two classifiers of support vector machine,However, each two-classifier of support vector machine has different classification accuracy for different eigenvector combinations. A particle swarm optimization (PSO) based feature optimization method is proposed.Experimental results show that the proposed method improves the accuracy of analog circuit fault diagnosis.Study on Lithium Ion Battery Life Prediction method.According to the principle of Li-ion battery life prediction, the whole frame of Li-ion battery life prediction is put forward.Firstly, Verhulst model is used as the life degradation model of lithium ion battery.Because the prediction accuracy of traditional Verhulst model is not high, particle swarm optimization (PSO) algorithm is proposed to optimize the traditional model and improve the prediction accuracy.Secondly, the parameters of the Verhulst model are estimated, and the particle swarm optimization algorithm is proposed to search the parameters of the model.Finally, in order to reduce the influence of noise on the prediction results, particle filter is used to update the model parameters.The experimental results show that the proposed method can predict the residual life of lithium ion battery with small error.Software design of diagnosis and prediction system.In order to meet the need of fault diagnosis and prediction of electronic system, the software of fault diagnosis and prediction system of electronic system is designed.Support vector machine (SVM) classification algorithm is integrated in the diagnosis system to calculate the fault features of the external data automatically, and the results of fault diagnosis are given quickly.The prediction system integrates several commonly used prediction models, such as the Verhulst model and the AR model. The prediction system can automatically select the appropriate prediction algorithm for the external reading data series, and give the prediction results.The experimental data show that the diagnosis and prediction system can give accurate results efficiently.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN710;TM912

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