局部均值分解及其在機(jī)械故障診斷中的應(yīng)用研究
發(fā)布時間：2018-07-28 08:53
【摘要】：工程中故障振動信號一般都為非線性非穩(wěn)態(tài)的信號,其振動頻率會隨著時間不斷的發(fā)生變化,傳統(tǒng)的傅里葉變換(Fourier Transform, FT)只能得到振動信號的頻率分布范圍,不能辨別信號頻率發(fā)生突變的時間點(diǎn)。短時傅里葉變換及后來發(fā)展起來的Gabor變換還有同期發(fā)展起來的Wignar-Ville分布(WVD)都是利用窗函數(shù)對原始信號進(jìn)行截取,且其所加窗函數(shù)是固定不變的,在分析多分量信號時,WVD在時頻譜上會出現(xiàn)交叉項(xiàng)。小波變換的窗函數(shù)寬度可以隨其在時間軸上平移而發(fā)生變化,但小波變換本質(zhì)上是對原始信號的機(jī)械截取,仍缺乏對信號分解的自適應(yīng)性。1998年N.E. Huang提出一種新的時頻分析方法希爾伯特-黃變換,2005年J. S.Smith提出另一種新的時頻分析方法方法——局部均值分解(Local Mean Decomposition, LMD),都是對以傅里葉變換為基礎(chǔ)的線性穩(wěn)態(tài)譜分析的一個重大突破。LMD是一種自適應(yīng)的時頻分析方法,可以將振動信號分解為一組頻率從高到低自動排列的乘積函數(shù)(Product Function, PF), PF分量是一個包絡(luò)值為1的調(diào)頻函數(shù)和一個包絡(luò)函數(shù)的乘積。LMD方法對信號的分解方式很好地解決了以往時頻分析方法對信號的機(jī)械劃分的問題,從而使分解結(jié)果更準(zhǔn)確。文章對LMD方法原理及方法中存在的問題進(jìn)行了詳細(xì)的介紹,并著重針對LMD方法的端點(diǎn)效應(yīng)進(jìn)行了改進(jìn)。將LMD方法與Teager能量算子和1.5維譜相結(jié)合,使?jié)L動軸承的故障特征頻率更明顯；LMD方法與增強(qiáng)包絡(luò)譜結(jié)合,并提出新的PF分量篩選準(zhǔn)則,使軸承故障特征頻率更容易辨別。隨后將LMD方法與支持向量機(jī)(Support Vector Machine, SVM)相結(jié)合,同時提出新的特征值提取方法提取故障特征,用SVM對這些故障特征進(jìn)行分類,利用該方法對故障軸承的故障程度和轉(zhuǎn)子碰摩位置進(jìn)行了識別,都取得了良好的效果,驗(yàn)證了所提方法的可行性。
[Abstract]:In engineering, the fault vibration signal is usually nonlinear and unsteady, and its vibration frequency will change with time. The traditional Fourier transform (Fourier Transform, FT) can only get the frequency distribution range of the vibration signal. The time point at which the frequency of the signal is mutated cannot be distinguished. The short time Fourier transform (STFT), the later developed Gabor transform and the Wignar-Ville distribution (WVD) developed in the same period are all used to intercept the original signal by window function, and the added window function is fixed and invariant. When analyzing the multicomponent signal, the WVD will appear cross term in the time spectrum. The width of window function of wavelet transform can be changed with its translation on the time axis, but wavelet transform is essentially mechanical interception of the original signal. In 1998, N. E. Huang proposed a new time-frequency analysis method, Hilbert-Huang transform. In 2005, J. S.Smith proposed another new time-frequency analysis method-local mean decomposition (Local Mean Decomposition, LMD),. An important breakthrough in linear steady-state spectrum analysis based on Fourier transform. LMD is an adaptive time-frequency analysis method. The vibration signal can be decomposed into a set of product functions with frequency from high to low, which is the product of a frequency modulation function with an envelope value of 1 and an envelope function. LMD method is a good way to decompose the signal. The problem of mechanical division of signals in previous time-frequency analysis methods is solved. Thus, the decomposition result is more accurate. In this paper, the principle and problems of LMD method are introduced in detail, and the endpoint effect of LMD method is improved. By combining the LMD method with the Teager energy operator and 1.5-D spectrum, the fault characteristic frequency of rolling bearing is more obvious than that of the enhanced envelope spectrum. A new PF component screening criterion is proposed to distinguish the fault feature frequency of the bearing more easily. Then the LMD method is combined with the support vector machine (Support Vector Machine, SVM), and a new feature extraction method is proposed to extract the fault features. The fault features are classified by SVM. The method is used to identify the fault degree of the bearing and the rubbing position of the rotor. Good results are obtained and the feasibility of the proposed method is verified.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TH17
[Abstract]:In engineering, the fault vibration signal is usually nonlinear and unsteady, and its vibration frequency will change with time. The traditional Fourier transform (Fourier Transform, FT) can only get the frequency distribution range of the vibration signal. The time point at which the frequency of the signal is mutated cannot be distinguished. The short time Fourier transform (STFT), the later developed Gabor transform and the Wignar-Ville distribution (WVD) developed in the same period are all used to intercept the original signal by window function, and the added window function is fixed and invariant. When analyzing the multicomponent signal, the WVD will appear cross term in the time spectrum. The width of window function of wavelet transform can be changed with its translation on the time axis, but wavelet transform is essentially mechanical interception of the original signal. In 1998, N. E. Huang proposed a new time-frequency analysis method, Hilbert-Huang transform. In 2005, J. S.Smith proposed another new time-frequency analysis method-local mean decomposition (Local Mean Decomposition, LMD),. An important breakthrough in linear steady-state spectrum analysis based on Fourier transform. LMD is an adaptive time-frequency analysis method. The vibration signal can be decomposed into a set of product functions with frequency from high to low, which is the product of a frequency modulation function with an envelope value of 1 and an envelope function. LMD method is a good way to decompose the signal. The problem of mechanical division of signals in previous time-frequency analysis methods is solved. Thus, the decomposition result is more accurate. In this paper, the principle and problems of LMD method are introduced in detail, and the endpoint effect of LMD method is improved. By combining the LMD method with the Teager energy operator and 1.5-D spectrum, the fault characteristic frequency of rolling bearing is more obvious than that of the enhanced envelope spectrum. A new PF component screening criterion is proposed to distinguish the fault feature frequency of the bearing more easily. Then the LMD method is combined with the support vector machine (Support Vector Machine, SVM), and a new feature extraction method is proposed to extract the fault features. The fault features are classified by SVM. The method is used to identify the fault degree of the bearing and the rubbing position of the rotor. Good results are obtained and the feasibility of the proposed method is verified.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TH17
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