基于時間序列標(biāo)度分析的旋轉(zhuǎn)機(jī)械故障診斷方法研究
發(fā)布時間：2018-09-04 07:18
【摘要】：機(jī)械故障診斷的關(guān)鍵問題是故障特征提取。機(jī)械故障信號通常具有強(qiáng)烈的非平穩(wěn)和非線性特征，本文在總結(jié)現(xiàn)有機(jī)械故障診斷方法優(yōu)缺點的基礎(chǔ)上，采用統(tǒng)計物理學(xué)上的標(biāo)度分析方法來研究復(fù)雜機(jī)械故障信號的波動狀況，提出了基于時間序列標(biāo)度分析的旋轉(zhuǎn)機(jī)械故障診斷方法。本文從一個新的角度來研究機(jī)械故障診斷問題，形成了具有學(xué)科交叉特色的機(jī)械故障診斷方法。本文的研究工作主要包括以下六個部分： (1)受自然界大量存在的標(biāo)度曲線轉(zhuǎn)折現(xiàn)象的啟發(fā)，本文將時間序列的多標(biāo)度指數(shù)作為機(jī)械故障信號的故障特征，提出了基于時間序列多標(biāo)度指數(shù)特征的機(jī)械故障特征提取方法。利用齒輪箱和滾動軸承故障數(shù)據(jù)對該方法的性能進(jìn)行了驗證，，結(jié)果證明了該方法的有效性。 (2)針對原始序列標(biāo)度曲線的特征參數(shù)難以提取的問題，本文采用增量序列的波動特征來表達(dá)機(jī)械系統(tǒng)的動力學(xué)行為，提出了基于增量序列標(biāo)度特征的機(jī)械故障診斷方法。利用齒輪箱和滾動軸承故障數(shù)據(jù)對該方法的性能進(jìn)行了驗證，結(jié)果證明了該方法的有效性。 (3)通過分析從增量序列標(biāo)度曲線上提取的數(shù)據(jù)點在坐標(biāo)圖上的分布特征，本文發(fā)現(xiàn)故障狀態(tài)所對應(yīng)的數(shù)據(jù)點可以近似擬合為一條直線，而正常狀態(tài)所對應(yīng)的數(shù)據(jù)點則明顯地偏離這條直線。為了描述這種有趣的現(xiàn)象，提出了“故障線”的概念，隨后探討了“故障線”的成因。 (4)針對齒輪箱故障信號所具有的非平穩(wěn)和非線性特點，本文提出了基于時間序列多重分形特征的齒輪箱故障特征提取方法。該方法采用MF-DFA計算齒輪箱故障信號的多重分形譜，然后利用從多重分形譜上提取的特征參數(shù)對齒輪箱進(jìn)行故障診斷。利用齒輪箱故障數(shù)據(jù)對該方法的性能進(jìn)行了驗證，結(jié)果證明了該方法的有效性。 (5)為了解決滾動軸承的故障類型和損傷程度難以識別的問題，本文提出了基于MF-DFA和馬氏距離判別法的滾動軸承故障診斷方法。該方法利用MF-DFA計算軸承故障信號的多重分形譜，從多重分形譜上提取特征參數(shù)，然后采用馬氏距離判別法對這些特征參數(shù)進(jìn)行分類。利用滾動軸承故障數(shù)據(jù)對該方法的性能進(jìn)行了驗證，結(jié)果證明了該方法的有效性。 (6)本文對旋轉(zhuǎn)機(jī)械振動信號出現(xiàn)多重分形的原因進(jìn)行了研究。通過比較原始數(shù)據(jù)及其重排數(shù)據(jù)和替代數(shù)據(jù)的廣義Hurst指數(shù)曲線，本文確定了數(shù)據(jù)波動的內(nèi)在長程相關(guān)性是導(dǎo)致齒輪箱和滾動軸承振動信號出現(xiàn)多重分形的主要原因。
[Abstract]:The key problem of mechanical fault diagnosis is fault feature extraction. Mechanical fault signals usually have strong nonstationary and nonlinear characteristics. This paper summarizes the advantages and disadvantages of existing mechanical fault diagnosis methods. In this paper, the scale analysis method in statistical physics is used to study the fluctuation of complex mechanical fault signals, and a fault diagnosis method for rotating machinery based on time series scale analysis is proposed. In this paper, the problem of mechanical fault diagnosis is studied from a new point of view, and a method of mechanical fault diagnosis with interdisciplinary characteristics is formed. The research work of this paper mainly includes the following six parts: (1) inspired by the phenomenon of scale curve turning in nature, the multi-scale exponent of time series is regarded as the fault feature of mechanical fault signal. A method of mechanical fault feature extraction based on multi-scale exponential feature of time series is proposed. The performance of the method is verified by using the gearbox and rolling bearing fault data. The results show that the method is effective. (2) the characteristic parameters of the original series scale curve are difficult to extract. In this paper, the dynamic behavior of mechanical systems is expressed by the fluctuation characteristics of incremental sequences, and a mechanical fault diagnosis method based on the scaling features of incremental sequences is proposed. The performance of the method is verified by using gearbox and rolling bearing fault data. The results show that the method is effective. (3) the distribution characteristics of the data points extracted from the incremental series scale curve on the coordinate diagram are analyzed. In this paper, it is found that the data points corresponding to the fault state can be approximately fitted into a straight line, while the data points corresponding to the normal state deviate from the line obviously. In order to describe this interesting phenomenon, the concept of "fault line" is proposed, and the causes of "fault line" are discussed. (4) aiming at the non-stationary and nonlinear characteristics of gearbox fault signal, In this paper, a method of gearbox fault feature extraction based on time series multifractal feature is proposed. This method uses MF-DFA to calculate the multifractal spectrum of the gearbox fault signal, and then uses the characteristic parameters extracted from the multifractal spectrum to diagnose the gearbox fault. The performance of the method is verified by using gearbox fault data. The results show that the method is effective. (5) in order to solve the problem that the fault type and damage degree of rolling bearing are difficult to identify, This paper presents a fault diagnosis method for rolling bearings based on MF-DFA and Markov distance discrimination. In this method, the multifractal spectrum of bearing fault signal is calculated by MF-DFA, and the characteristic parameters are extracted from the multifractal spectrum, and then these parameters are classified by Markov distance discriminant method. The performance of the method is verified by the rolling bearing fault data. The results show that the method is effective. (6) the reason of multifractal of vibration signal of rotating machinery is studied in this paper. By comparing the generalized Hurst exponent curves of raw data, rearrangement data and substitute data, this paper determines that the inherent long range correlation of data fluctuation is the main cause of multifractal of vibration signals of gearbox and rolling bearing.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TH165.3
本文編號：2221384
[Abstract]:The key problem of mechanical fault diagnosis is fault feature extraction. Mechanical fault signals usually have strong nonstationary and nonlinear characteristics. This paper summarizes the advantages and disadvantages of existing mechanical fault diagnosis methods. In this paper, the scale analysis method in statistical physics is used to study the fluctuation of complex mechanical fault signals, and a fault diagnosis method for rotating machinery based on time series scale analysis is proposed. In this paper, the problem of mechanical fault diagnosis is studied from a new point of view, and a method of mechanical fault diagnosis with interdisciplinary characteristics is formed. The research work of this paper mainly includes the following six parts: (1) inspired by the phenomenon of scale curve turning in nature, the multi-scale exponent of time series is regarded as the fault feature of mechanical fault signal. A method of mechanical fault feature extraction based on multi-scale exponential feature of time series is proposed. The performance of the method is verified by using the gearbox and rolling bearing fault data. The results show that the method is effective. (2) the characteristic parameters of the original series scale curve are difficult to extract. In this paper, the dynamic behavior of mechanical systems is expressed by the fluctuation characteristics of incremental sequences, and a mechanical fault diagnosis method based on the scaling features of incremental sequences is proposed. The performance of the method is verified by using gearbox and rolling bearing fault data. The results show that the method is effective. (3) the distribution characteristics of the data points extracted from the incremental series scale curve on the coordinate diagram are analyzed. In this paper, it is found that the data points corresponding to the fault state can be approximately fitted into a straight line, while the data points corresponding to the normal state deviate from the line obviously. In order to describe this interesting phenomenon, the concept of "fault line" is proposed, and the causes of "fault line" are discussed. (4) aiming at the non-stationary and nonlinear characteristics of gearbox fault signal, In this paper, a method of gearbox fault feature extraction based on time series multifractal feature is proposed. This method uses MF-DFA to calculate the multifractal spectrum of the gearbox fault signal, and then uses the characteristic parameters extracted from the multifractal spectrum to diagnose the gearbox fault. The performance of the method is verified by using gearbox fault data. The results show that the method is effective. (5) in order to solve the problem that the fault type and damage degree of rolling bearing are difficult to identify, This paper presents a fault diagnosis method for rolling bearings based on MF-DFA and Markov distance discrimination. In this method, the multifractal spectrum of bearing fault signal is calculated by MF-DFA, and the characteristic parameters are extracted from the multifractal spectrum, and then these parameters are classified by Markov distance discriminant method. The performance of the method is verified by the rolling bearing fault data. The results show that the method is effective. (6) the reason of multifractal of vibration signal of rotating machinery is studied in this paper. By comparing the generalized Hurst exponent curves of raw data, rearrangement data and substitute data, this paper determines that the inherent long range correlation of data fluctuation is the main cause of multifractal of vibration signals of gearbox and rolling bearing.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TH165.3
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