FARIMA模型在復雜機械系統(tǒng)的故障診斷中的應用
發(fā)布時間：2019-01-05 10:54
【摘要】：長期以來對復雜的機械系統(tǒng)，人們希望能夠及時、準確地發(fā)現故障、判斷故障的損傷程度，并且做出評估與預測，因此故障診斷技術也隨之越來越受到重視，并且在工業(yè)領域及信號檢測領域是很有價值的課題。時間序列分析是一種經典的分析方法，在故障診斷中有獨特的優(yōu)勢，，大多數情況是對振動信號建立ARMA模型并進行分析，但是這種方法有一定的局限性。注意到很多情形的振動信號的具有長記憶特性，本文嘗試用FARIMA模型對故障診斷問題進行分析。 本文的目的是引入體現長記憶特征的模型，通過實例證明FARIMA模型比傳統(tǒng)的ARMA模型對模擬故障診斷的振動數據更為精確。本文對FARIMA模型的長記憶特性和分數差分的兩個特性進行了分析，從理論上說明了用FARIMA建模的條件和優(yōu)勢。本文詳細敘述了平穩(wěn)時間序列的建模步驟、FARIMA模型的建模步驟，并且總結了參數估計的方法，說明了FARIMA模型雖然是ARMA模型的推廣但是它們之間有很大的不同。通過對Bently實驗臺得到的汽輪機轉子的振動信號進行分析，結合MATLAB、SAS軟件實現的模擬結果和對參數的估計結果，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行振動信號的分析。在建立FARIMA模型的時候，考慮了非高斯噪音擾動的S α S-FARIMA模型和隨時間變化參數會發(fā)生改變的T-V-FARIMA模型，這兩種特殊的情況反映了FARIMA模型對某些實際數據進行建模的靈活性和有效性，同時給出了修正這個模型的方向。
[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.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3;O211.61
本文編號：2401683
[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.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH165.3;O211.61
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相關期刊論文 前3條
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