本文選題：模擬電路　切入點(diǎn)：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(jì),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(diǎn)和難點(diǎn)。另外,隨著電動(dòng)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(dòng)汽車性能和安全的關(guān)鍵。因此,鋰離子電池壽命預(yù)測(cè)是電子系統(tǒng)健康管理的另一個(gè)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(jī)進(jìn)行模式分類時(shí)的本質(zhì),提出了采用統(tǒng)計(jì)特征量:均值、方差、標(biāo)準(zhǔn)偏差、熵、峭度、偏斜度和形心來組成故障特征向量。同時(shí),針對(duì)目前支持向量機(jī)進(jìn)行模式分類時(shí)的缺陷,即采用同一個(gè)特征向量組合來訓(xùn)練支持向量機(jī)所有的二分類器,然而支持向量機(jī)的每個(gè)二分類器對(duì)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(shí)驗(yàn)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(cè)方法的研究。根據(jù)鋰離子電池壽命預(yù)測(cè)的原理,提出了鋰離子電池壽命預(yù)測(cè)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(cè)精度不高,提出了采用粒子群算法對(duì)傳統(tǒng)的模型進(jìn)行優(yōu)化,提高了預(yù)測(cè)精度。其次,估計(jì)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(duì)預(yù)測(cè)結(jié)果的影響,采用粒子濾波對(duì)模型參數(shù)進(jìn)行更新。實(shí)驗(yàn)結(jié)果表明提出的方法可以以較小的誤差預(yù)測(cè)出鋰離子電池的剩余壽命。3.診斷與預(yù)測(cè)系統(tǒng)的軟件設(shè)計(jì)。為了滿足電子系統(tǒng)故障診斷與故障預(yù)測(cè)的需求,分別設(shè)計(jì)了電子系統(tǒng)故障診斷與預(yù)測(cè)系統(tǒng)的軟件。診斷系統(tǒng)集成了支持向量機(jī)分類算法,對(duì)外部讀入的數(shù)據(jù)自動(dòng)進(jìn)行故障特征的計(jì)算,并迅速給出故障診斷的結(jié)果。預(yù)測(cè)系統(tǒng)集成了多個(gè)常用的預(yù)測(cè)模型,如Verhulst模型、GM(1,1)模型、AR模型,對(duì)于外部讀入的數(shù)據(jù)序列,可以自動(dòng)優(yōu)選出合適的預(yù)測(cè)算法,并給出預(yù)測(cè)結(jié)果。經(jīng)實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證,診斷與預(yù)測(cè)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN710;TM912

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

1 莫郁薇;張?jiān)稣?古文剛;聶國(guó)健;李志國(guó);;多芯片組件MCM的失效率預(yù)計(jì)研究[J];半導(dǎo)體技術(shù);2006年03期

2 榮寶輝;王曉燕;安振峰;仲琳;陳國(guó)鷹;張存善;;大功率半導(dǎo)體激光器加速壽命測(cè)試方法[J];半導(dǎo)體技術(shù);2008年04期

3 劉曉紅;郭文勝;王合利;常青松;;集成壓控振蕩器的可靠性設(shè)計(jì)與分析[J];半導(dǎo)體技術(shù);2008年05期

4 劉曉紅;常青松;;混合集成電路內(nèi)部多余物的控制研究[J];半導(dǎo)體技術(shù);2008年07期

5 蔡偉智;;功率發(fā)光二極管的壽命預(yù)測(cè)[J];半導(dǎo)體技術(shù);2008年10期

6 黃雒光;董四華;劉英坤;郎秀蘭;;L波段Si微波脈沖功率晶體管射頻加速壽命試驗(yàn)[J];半導(dǎo)體技術(shù);2009年05期

7 林潔馨;楊發(fā)順;馬奎;龔紅;;提高功率開關(guān)晶體管高溫反偏能力的研究[J];半導(dǎo)體技術(shù);2010年02期

8 童亮;彭浩;高金環(huán);黃杰;;Si微波功率晶體管加速壽命試驗(yàn)夾具的設(shè)計(jì)[J];半導(dǎo)體技術(shù);2011年08期

9 童亮;彭浩;高金環(huán);黃杰;;S波段硅微波功率晶體管的加速壽命試驗(yàn)[J];半導(dǎo)體技術(shù);2012年04期

10 潘文庚;王曉鳴;陶敏;李文彬;;長(zhǎng)貯彈藥失效機(jī)理分析研究[J];彈箭與制導(dǎo)學(xué)報(bào);2007年01期

相關(guān)會(huì)議論文 前7條

1 李曉潮;;一種4k×4k CCD的加速壽命試驗(yàn)方法[A];中國(guó)光學(xué)學(xué)會(huì)2010年光學(xué)大會(huì)論文集[C];2010年

2 吳琦;魏建中;;微處理器(CPU)的穩(wěn)態(tài)電離輻射(總劑量)試驗(yàn)[A];第十一屆全國(guó)可靠性物理學(xué)術(shù)討論會(huì)論文集[C];2005年

3 蔡偉智;;半導(dǎo)體照明光源——發(fā)光二極管失效機(jī)理探討[A];2007年中國(guó)（廈門）LED照明與裝飾論壇暨城市夜景建設(shè)研討會(huì)論文集[C];2007年

4 肖體鵬;周衛(wèi)東;劉健;;PPS有機(jī)電容器的研究進(jìn)展[A];中國(guó)電子元件行業(yè)協(xié)會(huì)電容器分會(huì)有機(jī)電容器專業(yè)委員會(huì)第十屆技術(shù)交流會(huì)論文集[C];2008年

5 付琬月;董宇亮;孫才明;毋俊s，

本文編號(hào)：1716046