本文選題：故障診斷 + 神經(jīng)網(wǎng)絡(luò)　； 參考：《湖南師范大學(xué)》2015年碩士論文

【摘要】：模擬電路故障診斷技術(shù)的研究開始于1960年,目前已在國內(nèi)外取得了大量有效的科研成果,逐步形成了完善的系統(tǒng)理論,在電路理論中占據(jù)非常重要的地位。同時(shí),隨著電子工業(yè)的飛速發(fā)展,電器設(shè)備的集成度越來越高,日趨模塊化和功能化。但是由于模擬電路自身存在的非線性、連續(xù)性、元器件參數(shù)容差等特性使得模擬電路故障診斷的難度非常大。采用傳統(tǒng)的模擬電路故障診斷方法已難以滿足實(shí)際工程應(yīng)用的需求,所以亟需探求新的現(xiàn)代化模擬電路故障診斷技術(shù)。諸如神經(jīng)網(wǎng)絡(luò)、小波分析、模糊理論、遺傳算法等人工智能技術(shù)的出現(xiàn)和發(fā)展,形成了這一領(lǐng)域新的研究方向。針對(duì)模擬電路故障診斷的模糊性和不確定性等問題,采用人工智能新技術(shù)的現(xiàn)代模擬電路故障診斷方法為常規(guī)方法所不能解決的各類問題帶來了新的解決思路。本文系統(tǒng)地分析了幾類傳統(tǒng)的模擬電路故障診斷方法以及基于智能理論的現(xiàn)代模擬電路故障診斷方法。在此基礎(chǔ)上,研究了將BP神經(jīng)網(wǎng)絡(luò)、小波分析、小波包分析等理論應(yīng)用于模擬電路故障診斷中的方法,并引入改進(jìn)粒子群優(yōu)化算法優(yōu)化神經(jīng)網(wǎng)絡(luò)的連接權(quán)值,達(dá)到加快網(wǎng)絡(luò)收斂速度和提高診斷正確率的目的,進(jìn)一步提升網(wǎng)絡(luò)的性能。主要工作有:(1)闡述了模擬電路故障診斷課題的背景意義及當(dāng)前國內(nèi)外的發(fā)展?fàn)顩r,總結(jié)了傳統(tǒng)的故障診斷技術(shù)以及近年來發(fā)展較快的智能故障診斷技術(shù);(2)系統(tǒng)的研究了神經(jīng)網(wǎng)絡(luò)、小波分析、小波包分析等理論知識(shí),探索了將這幾種技術(shù)應(yīng)用于模擬電路故障診斷中的方法,并選取待測電路進(jìn)行了仿真分析,用實(shí)例證明了該方法的有效性與可行性;(3)對(duì)模擬電路故障診斷中最為關(guān)鍵的技術(shù)——特征向量的提取進(jìn)行了詳盡的分析與研究。應(yīng)用小波多分辨分析和小波包分析等技術(shù)提取故障特征,并進(jìn)一步探索將兩種方法提取的故障特征向量融合成新的特征向量,作為故障診斷的故障集。通過對(duì)待測電路做實(shí)例研究的診斷結(jié)果表明了此方法的優(yōu)異性;(4)將粒子群算法引入基于小波神經(jīng)網(wǎng)絡(luò)的模擬電路故障診斷中,利用改進(jìn)粒子群優(yōu)化算法對(duì)小波神經(jīng)網(wǎng)絡(luò)的連接權(quán)值進(jìn)行適當(dāng)?shù)膬?yōu)化,加快了神經(jīng)網(wǎng)絡(luò)的收斂速度,并且訓(xùn)練后的網(wǎng)絡(luò)具有較好的魯棒性。
[Abstract]:The research of analog circuit fault diagnosis technology began in 1960. At present, it has made a lot of effective scientific research achievements at home and abroad, and gradually formed a perfect system theory, which occupies a very important position in the circuit theory. At the same time, with the rapid development of electronic industry, the integration of electrical equipment is becoming more and more high, increasingly modular and functional. However, because of the nonlinearity, continuity and component tolerance of analog circuits, the fault diagnosis of analog circuits is very difficult. Traditional analog circuit fault diagnosis method is difficult to meet the needs of practical engineering applications, so it is urgent to explore a new modern analog circuit fault diagnosis technology. The emergence and development of artificial intelligence technology such as neural network, wavelet analysis, fuzzy theory and genetic algorithm have formed a new research direction in this field. In view of the fuzziness and uncertainty of analog circuit fault diagnosis, the modern analog circuit fault diagnosis method using new artificial intelligence technology brings a new solution to all kinds of problems that can not be solved by conventional method. In this paper, several traditional analog circuit fault diagnosis methods and modern analog circuit fault diagnosis methods based on intelligent theory are systematically analyzed. On this basis, the methods of applying BP neural network, wavelet analysis and wavelet packet analysis to fault diagnosis of analog circuits are studied, and an improved particle swarm optimization algorithm is introduced to optimize the connection weights of neural networks. It can speed up the convergence of the network and improve the diagnostic accuracy, and further improve the performance of the network. The main works are as follows: (1) the background significance of analog circuit fault diagnosis and the current development situation at home and abroad are expounded. The traditional fault diagnosis technology and the intelligent fault diagnosis technology developed rapidly in recent years are summarized. (2) the theoretical knowledge of neural network, wavelet analysis, wavelet packet analysis and so on are studied systematically. The methods of applying these techniques to the fault diagnosis of analog circuits are explored, and the circuits to be tested are selected for simulation analysis. The effectiveness and feasibility of this method are proved by an example. (3) the extraction of eigenvector, which is the most important technique in analog circuit fault diagnosis, is analyzed and studied in detail. Wavelet multi-resolution analysis and wavelet packet analysis are used to extract fault features. Furthermore, the fault feature vectors extracted by the two methods are fused into new feature vectors, which can be used as fault sets for fault diagnosis. The results show that the method is excellent. (4) the particle swarm optimization algorithm is introduced into the fault diagnosis of analog circuits based on wavelet neural network. The improved particle swarm optimization algorithm is used to optimize the connection weights of wavelet neural networks, which accelerates the convergence speed of neural networks, and the trained neural networks have better robustness.
【學(xué)位授予單位】：湖南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP183;TN710

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相關(guān)期刊論文 前2條

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本文編號(hào)：2062582