【摘要】：長(zhǎng)期以來(lái)對(duì)復(fù)雜的機(jī)械系統(tǒng)，人們希望能夠及時(shí)、準(zhǔn)確地發(fā)現(xiàn)故障、判斷故障的損傷程度，并且做出評(píng)估與預(yù)測(cè)，因此故障診斷技術(shù)也隨之越來(lái)越受到重視，并且在工業(yè)領(lǐng)域及信號(hào)檢測(cè)領(lǐng)域是很有價(jià)值的課題。時(shí)間序列分析是一種經(jīng)典的分析方法，在故障診斷中有獨(dú)特的優(yōu)勢(shì)，，大多數(shù)情況是對(duì)振動(dòng)信號(hào)建立ARMA模型并進(jìn)行分析，但是這種方法有一定的局限性。注意到很多情形的振動(dòng)信號(hào)的具有長(zhǎng)記憶特性，本文嘗試用FARIMA模型對(duì)故障診斷問(wèn)題進(jìn)行分析。 本文的目的是引入體現(xiàn)長(zhǎng)記憶特征的模型，通過(guò)實(shí)例證明FARIMA模型比傳統(tǒng)的ARMA模型對(duì)模擬故障診斷的振動(dòng)數(shù)據(jù)更為精確。本文對(duì)FARIMA模型的長(zhǎng)記憶特性和分?jǐn)?shù)差分的兩個(gè)特性進(jìn)行了分析，從理論上說(shuō)明了用FARIMA建模的條件和優(yōu)勢(shì)。本文詳細(xì)敘述了平穩(wěn)時(shí)間序列的建模步驟、FARIMA模型的建模步驟，并且總結(jié)了參數(shù)估計(jì)的方法，說(shuō)明了FARIMA模型雖然是ARMA模型的推廣但是它們之間有很大的不同。通過(guò)對(duì)Bently實(shí)驗(yàn)臺(tái)得到的汽輪機(jī)轉(zhuǎn)子的振動(dòng)信號(hào)進(jìn)行分析，結(jié)合MATLAB、SAS軟件實(shí)現(xiàn)的模擬結(jié)果和對(duì)參數(shù)的估計(jì)結(jié)果，本文顯示了這種建模方法比傳統(tǒng)的建模方法更能有效的模擬并且進(jìn)行振動(dòng)信號(hào)的分析。在建立FARIMA模型的時(shí)候，考慮了非高斯噪音擾動(dòng)的S α S-FARIMA模型和隨時(shí)間變化參數(shù)會(huì)發(fā)生改變的T-V-FARIMA模型，這兩種特殊的情況反映了FARIMA模型對(duì)某些實(shí)際數(shù)據(jù)進(jìn)行建模的靈活性和有效性，同時(shí)給出了修正這個(gè)模型的方向。
[Abstract]:For a long time, people hope to find the fault in time and accurately, judge the damage degree of the fault, and make the evaluation and prediction for the complex mechanical system. Therefore, the fault diagnosis technology has been paid more and more attention. And in the field of industry and signal detection is a very valuable subject. Time series analysis is a classical analysis method, which has unique advantages in fault diagnosis. In most cases, the ARMA model of vibration signal is established and analyzed, but this method has some limitations. Noting that many vibration signals have long memory characteristics, this paper attempts to use FARIMA model to analyze the problem of fault diagnosis. The purpose of this paper is to introduce a long memory model. It is proved that the FARIMA model is more accurate than the traditional ARMA model in simulating the vibration data of fault diagnosis. In this paper, the long memory characteristics of FARIMA model and the two characteristics of fractional difference are analyzed, and the conditions and advantages of FARIMA modeling are explained theoretically. In this paper, the modeling steps of stationary time series and FARIMA model are described in detail, and the methods of parameter estimation are summarized. It is shown that although FARIMA model is a generalization of ARMA model, there are great differences between them. By analyzing the vibration signals of the turbine rotor obtained from the Bently test bench, combining the simulation results of the MATLAB,SAS software and the estimation of the parameters, the vibration signals of the turbine rotor are analyzed. This paper shows that this modeling method is more effective than the traditional modeling method in simulating and analyzing vibration signals. In establishing the FARIMA model, the S 偽 S-FARIMA model with non-Gao Si noise disturbance and the T-V-FARIMA model with time-varying parameters are considered. These two special cases reflect the flexibility and validity of the FARIMA model for modeling some real data, and the direction of modifying the model is given.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3;O211.61

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