本文關(guān)鍵詞：基于經(jīng)驗(yàn)小波變換的模擬電路健康預(yù)測(cè)方法研究　出處：《合肥工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 故障特征提取 經(jīng)驗(yàn)小波變換 能量熵 模擬電路健康預(yù)測(cè) 電路健康指數(shù)

【摘要】：隨著電子技術(shù)的快速發(fā)展,模擬電路結(jié)構(gòu)日趨復(fù)雜,電路故障發(fā)生率越來越高。由于模擬電路在運(yùn)行過程中,其故障表現(xiàn)為非線性和不確定性,一般故障特征提取方法很難準(zhǔn)確并有效的提取電路的故障特征,利用新的技術(shù)進(jìn)行模擬電路故障特征提取成為必然的趨勢(shì)。經(jīng)驗(yàn)小波變換(EWT)是近幾年來發(fā)展起來的一種新的自適應(yīng)信號(hào)分析方法,其主要用于處理非平穩(wěn)、不確定隨機(jī)信號(hào)。為了獲得模擬電路最優(yōu)的故障特征,本文將EWT方法運(yùn)用到模擬電路故障特征提取中,提出了一種基于經(jīng)驗(yàn)小波變的模擬電路故障特征提取新方法。為了驗(yàn)證該方法,本文通過特定的電路進(jìn)行實(shí)驗(yàn)仿真,提取出了電路不同故障模式下故障信號(hào)的基頻分量,并結(jié)合信號(hào)的能量熵,成功的提取出了電路的故障特征。仿真結(jié)果表明了本文所提方法的可行性和有效性。目前,國內(nèi)外大多數(shù)學(xué)者都集中在模擬電路故障診斷等領(lǐng)域的研究,而對(duì)模擬電路健康預(yù)測(cè)方面的研究比較少見�，F(xiàn)有的故障預(yù)測(cè)方法所提取出的故障特征數(shù)據(jù)不能很好的反應(yīng)電路元件的退化程度,無法有效的對(duì)模擬電路的健康狀態(tài)進(jìn)行準(zhǔn)確預(yù)測(cè)。由此,本文在經(jīng)驗(yàn)小波變換方法的基礎(chǔ)上,提出了表征電路元件參數(shù)退化程度的電路健康指數(shù)(HI)的概念,研究了模擬電路故障健康指數(shù)與故障特征之間的量化關(guān)系,建立了模擬電路健康狀態(tài)的評(píng)價(jià)機(jī)制,結(jié)合BP神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型,完成了模擬電路的健康預(yù)測(cè)。最后通過相關(guān)電路進(jìn)行了實(shí)驗(yàn)驗(yàn)證,實(shí)驗(yàn)結(jié)果表明:基于EWT的模擬電路健康方法預(yù)測(cè)出的數(shù)據(jù)與元實(shí)際數(shù)據(jù)誤差較小,能夠比較準(zhǔn)確的對(duì)元件故障退化情況進(jìn)行預(yù)測(cè),非常適合用于實(shí)際模擬電路的健康預(yù)測(cè)。
[Abstract]:With the rapid development of electronic technology, the structure of analog circuits is becoming more and more complex, and the incidence of circuit faults is becoming higher and higher. General fault feature extraction methods are difficult to extract the circuit fault features accurately and effectively. Using new techniques to extract fault features of analog circuits becomes an inevitable trend. Empirical wavelet transform (EWT) is a new adaptive signal analysis method developed in recent years. It is mainly used to deal with non-stationary and uncertain random signals. In order to obtain the optimal fault characteristics of analog circuits, this paper applies the EWT method to the fault feature extraction of analog circuits. A new method of fault feature extraction for analog circuits based on empirical wavelet transform is proposed. In order to verify this method, experimental simulation is carried out through specific circuits. The fundamental frequency components of the fault signal in different fault modes of the circuit are extracted and combined with the energy entropy of the signal. The simulation results show that the proposed method is feasible and effective. At present, most scholars at home and abroad focus on analog circuit fault diagnosis and other areas of research. However, the research on analog circuit health prediction is rare. The fault characteristic data extracted by existing fault prediction methods can not well reflect the degradation degree of circuit components. The health state of analog circuits can not be predicted accurately. Therefore, this paper based on empirical wavelet transform method. The concept of circuit health index (HI), which is used to characterize the degradation of circuit component parameters, is proposed, and the quantitative relationship between fault health index and fault characteristics of analog circuits is studied. The health evaluation mechanism of analog circuit is established, and the model of BP neural network is used to predict the health of analog circuit. The experimental results show that the data predicted by the health method of analog circuit based on EWT has less error than the actual data, and it can accurately predict the degradation of component faults. It is very suitable for health prediction of practical analog circuits.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN710

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