基于時頻分析和復(fù)數(shù)域的模擬電路故障診斷研究
發(fā)布時間：2018-08-17 18:42
【摘要】：面對電路的高速集成和規(guī)模化,人們迫切的需要研究更加先進(jìn)的、高效的、智能的模擬電路故障診斷理論和技術(shù),以此滿足現(xiàn)代電子工業(yè)領(lǐng)域?qū)﹄娮酉到y(tǒng)安全性、可靠性及可測試性的高要求。其中,如何提取出故障的可辯特征以避免因故障之間的混疊造成故障的不可分已成為模擬電路故障診斷的研究焦點(diǎn)。時頻分析方法能在時間和頻率上同時提供信號的分布信息,能清晰的刻度任意時刻的信號頻率和幅值。復(fù)數(shù)域分析通過把時域信號變換到復(fù)數(shù)域,用實(shí)部和虛部來聯(lián)合刻畫信號在復(fù)平面的分布狀況。這兩種方法都能很好的為故障診斷提供更多可辨的細(xì)節(jié)特征。本文圍繞時頻分析技術(shù)和復(fù)數(shù)域分析進(jìn)行深入研究了模擬電路的參數(shù)故障診斷并提出了新的診斷方法。另外本文也對電子系統(tǒng)測試性驗(yàn)證做了一定的深入研究。本文取得的成果如下:(1)提出了基于集合經(jīng)驗(yàn)?zāi)B(tài)分解和極限學(xué)習(xí)機(jī)(EEMD-ELM)的模擬電路故障診斷方法。通過研究希爾伯特-黃變換以及其經(jīng)驗(yàn)?zāi)B(tài)分解變換的原理,將EEMD變換引入到模擬電路故障診斷中。由此提出了一種EEMD聯(lián)合相對熵和峭度構(gòu)建模擬電路故障特征向量,通過ELM進(jìn)行單、多參數(shù)故障診斷的方法。首先通過分別采集電路正常狀態(tài)和故障狀態(tài)響應(yīng)輸出信號,然后使用EEMD把響應(yīng)輸出各自分解成一組本征模態(tài)函數(shù)(IMF),計算出所有電路狀態(tài)IMF的峭度及電路正常狀態(tài)IMF和故障狀態(tài)IMF之間的相對熵,構(gòu)建成故障特征向量以此作為ELM的輸入樣本進(jìn)行故障的診斷。仿真結(jié)果表明該基于EEMD分解所建立的故障特征在ELM分類器中具有很低的計算代價和較高的診斷率。(2)對局部均值分解(LMD)的模擬電路故障特征向量優(yōu)化進(jìn)行了研究,提出了新的基于聚類方式的優(yōu)化策略。本文深入的研究了LMD的原理及分解過程。并采用LMD技術(shù)把被測電路的響應(yīng)輸出信號分解成一系列的乘積函數(shù)(PF)信號;以各PF信號的能量熵、標(biāo)準(zhǔn)差和峭度等參數(shù)構(gòu)造特征向量,這種特征向量的維度會隨著信號分解的PF個數(shù)的增加而增加。因此本文提出一種新的特征優(yōu)化策略對其進(jìn)行降維,首先以每種故障模式的各特征項(xiàng)的聚類中心和半徑為依據(jù),通過優(yōu)化策略從這些特征項(xiàng)中選出最優(yōu)特征項(xiàng),組成新的特征向量,以此作為LVQ神經(jīng)網(wǎng)絡(luò)的輸入。仿真實(shí)驗(yàn)證明該策略能使故障特征向量維數(shù)和分類器計算量有效的降低,同時也能有效進(jìn)行故障診斷。(3)提出了基于最小二乘圓擬合算法的復(fù)數(shù)域故障建模及故障診斷方法。實(shí)際模擬電路的故障響應(yīng)是連續(xù)無窮的,但是在傳統(tǒng)的故障診斷模型字典法中所存儲的故障特征值是離散的,這必然造成字典中故障類型的不完備。為了提高字典的完備性和解決元件容差影響,通過深入研究斜率故障模型和復(fù)數(shù)域故障模型,并在模擬電路復(fù)數(shù)域故障建模理論基礎(chǔ)上提出使用最小二乘圓擬合算法擬合出故障特征函數(shù)作為故障特征,并針對該模型提出了相應(yīng)的故障診斷方法,仿真實(shí)驗(yàn)和實(shí)際電路實(shí)驗(yàn)都驗(yàn)證了該方法能很好的實(shí)現(xiàn)故障診斷。(4)基于蟻群算法的故障樣本選擇。在傳統(tǒng)的測試驗(yàn)證實(shí)驗(yàn)中,基于故障率的隨機(jī)測試樣本選擇方法往往忽略了具有較小故障率的傳播故障的選擇,但是傳播故障一旦發(fā)生可能引起很嚴(yán)重的擴(kuò)散故障。為解決這個問題,提高對傳播故障的選擇,本文利用有向圖法和蟻群算法搜索傳播故障的最優(yōu)傳播路徑,然后為每個故障模塊(器件)建立后續(xù)傳播路徑集,根據(jù)新建的樣本選擇策略,優(yōu)化測試樣本。模擬實(shí)驗(yàn)仿真證明該策略能提高傳播故障被選擇率,提高測試能力和降低使用風(fēng)險。
[Abstract]:Facing the high-speed integration and large-scale of circuits, people urgently need to study more advanced, efficient and intelligent analog circuit fault diagnosis theory and technology, so as to meet the high requirements of electronic system security, reliability and testability in the field of modern electronic industry. Faults caused by overlapping between obstacles have become the focus of analog circuit fault diagnosis. Time-frequency analysis can provide the distribution information of signals at the same time in time and frequency, and can clearly scale the frequency and amplitude of signals at any time. Complex-domain analysis transforms the time-domain signals into complex-domain signals, using real and imaginary parts. Both of them can provide more distinguishable detail features for fault diagnosis. This paper focuses on time-frequency analysis and complex domain analysis to study the parameter fault diagnosis of analog circuits and proposes a new diagnosis method. The results are as follows: (1) A fault diagnosis method for analog circuits based on set empirical mode decomposition (EMD) and Extreme Learning Machine (EEMD-ELM) is proposed. In this paper, a method of constructing fault eigenvectors of analog circuits by EEMD combined with relative entropy and kurtosis is proposed. The single-parameter and multi-parameter fault diagnosis is performed by ELM. First, the output signals of normal state and fault state responses are collected respectively, and then the response outputs are decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The kurtosis of the circuit state IMF and the relative entropy between the circuit normal state IMF and the fault state IMF are constructed as fault feature vectors, which are used as input samples of the ELM for fault diagnosis. The fault eigenvector optimization of analog circuits based on local mean decomposition (LMD) is studied and a new optimization strategy based on clustering method is proposed.The principle and decomposition process of LMD are studied in detail.The response output signal of the circuit under test is decomposed into a series of product function (PF) signals by LMD technology. The dimension of eigenvectors will increase with the number of PFs decomposed by signals. Therefore, a new feature optimization strategy is proposed to reduce the dimension of eigenvectors. The simulation results show that this strategy can effectively reduce the dimension of fault feature vectors and the amount of classifier calculation, and also can effectively diagnose faults. (3) A complex domain fault modeling method based on least square circle fitting algorithm is proposed. Fault response of analog circuits is continuous and infinite, but the fault eigenvalues stored in the traditional fault diagnosis model dictionary are discrete, which inevitably leads to incomplete fault types in the dictionary. Based on the theory of fault modeling in complex domain of analog circuit, the least square circle fitting algorithm is proposed to fit the fault characteristic function as the fault characteristic, and the corresponding fault diagnosis method is proposed for the model. The simulation and actual circuit experiments show that the method can be well implemented. Fault diagnosis. (4) Fault sample selection based on ant colony algorithm. In traditional test validation experiments, random test sample selection method based on fault rate often ignores the selection of propagation faults with small fault rate, but propagation faults may cause very serious diffusion faults once they occur. In this paper, the directed graph method and ant colony algorithm are used to search the optimal propagation path of propagation faults, and then a set of subsequent propagation paths is established for each fault module (device). According to the new sample selection strategy, the test samples are optimized. Simulation results show that the strategy can improve the selection rate of propagation faults and test performance. Strength and reduce use risk.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TN710
本文編號：2188550
[Abstract]:Facing the high-speed integration and large-scale of circuits, people urgently need to study more advanced, efficient and intelligent analog circuit fault diagnosis theory and technology, so as to meet the high requirements of electronic system security, reliability and testability in the field of modern electronic industry. Faults caused by overlapping between obstacles have become the focus of analog circuit fault diagnosis. Time-frequency analysis can provide the distribution information of signals at the same time in time and frequency, and can clearly scale the frequency and amplitude of signals at any time. Complex-domain analysis transforms the time-domain signals into complex-domain signals, using real and imaginary parts. Both of them can provide more distinguishable detail features for fault diagnosis. This paper focuses on time-frequency analysis and complex domain analysis to study the parameter fault diagnosis of analog circuits and proposes a new diagnosis method. The results are as follows: (1) A fault diagnosis method for analog circuits based on set empirical mode decomposition (EMD) and Extreme Learning Machine (EEMD-ELM) is proposed. In this paper, a method of constructing fault eigenvectors of analog circuits by EEMD combined with relative entropy and kurtosis is proposed. The single-parameter and multi-parameter fault diagnosis is performed by ELM. First, the output signals of normal state and fault state responses are collected respectively, and then the response outputs are decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The kurtosis of the circuit state IMF and the relative entropy between the circuit normal state IMF and the fault state IMF are constructed as fault feature vectors, which are used as input samples of the ELM for fault diagnosis. The fault eigenvector optimization of analog circuits based on local mean decomposition (LMD) is studied and a new optimization strategy based on clustering method is proposed.The principle and decomposition process of LMD are studied in detail.The response output signal of the circuit under test is decomposed into a series of product function (PF) signals by LMD technology. The dimension of eigenvectors will increase with the number of PFs decomposed by signals. Therefore, a new feature optimization strategy is proposed to reduce the dimension of eigenvectors. The simulation results show that this strategy can effectively reduce the dimension of fault feature vectors and the amount of classifier calculation, and also can effectively diagnose faults. (3) A complex domain fault modeling method based on least square circle fitting algorithm is proposed. Fault response of analog circuits is continuous and infinite, but the fault eigenvalues stored in the traditional fault diagnosis model dictionary are discrete, which inevitably leads to incomplete fault types in the dictionary. Based on the theory of fault modeling in complex domain of analog circuit, the least square circle fitting algorithm is proposed to fit the fault characteristic function as the fault characteristic, and the corresponding fault diagnosis method is proposed for the model. The simulation and actual circuit experiments show that the method can be well implemented. Fault diagnosis. (4) Fault sample selection based on ant colony algorithm. In traditional test validation experiments, random test sample selection method based on fault rate often ignores the selection of propagation faults with small fault rate, but propagation faults may cause very serious diffusion faults once they occur. In this paper, the directed graph method and ant colony algorithm are used to search the optimal propagation path of propagation faults, and then a set of subsequent propagation paths is established for each fault module (device). According to the new sample selection strategy, the test samples are optimized. Simulation results show that the strategy can improve the selection rate of propagation faults and test performance. Strength and reduce use risk.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TN710
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