機械類畢業(yè)論文外文文獻資料翻譯

1、<p><b>　　外文文獻資料</b></p><p>　　At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. T

2、he transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time

3、 index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filt</p><p>　　Rotating machinery covers a broad range of mechanical equipments and plays an

4、important role in many industrial applications, such as aircraft engines, transmission systems, power plants, etc. Most of the machinery was operated by means of bearings, gearboxes and other rotating components, which m

5、ay develop faults. These faults may cause the machine to break down, resulting in significant economic loss and even catastrophic personal casualties. The study of rotating machine fault diagnosis has t</p><p&

6、gt;　　In order to extract or analyze the feature of the vibration signal, especially the transient, different techniques have been proposed for rotating machine diagnostics in the literature, such as empirical mode decomp

7、osition (EMD), independent component analysis (ICA), time–frequency representation (TFR), wavelet transform, and matching pursuit (MP), etc. EMD, as an adaptive decomposition technique proposed for nonlinear and nonstati

8、onary by Huang, has been developed and widely applied in rotating</p><p>　　EMD decomposes the complicated signal into a set of complete and almost orthogonal components named intrinsic mode function (IMF). H

9、owever, it still has some shortcomings when it comes to calculating instantaneous frequency or in some cases it may reveal plausible characteristics due to the mode mixing, and this shortcoming makes it untenable in effe

10、ctive application in transient detection and analysis. ICA is known as a powerful tool for blind source separation, which has also been introduced a</p><p>　　TFR is the most frequently used method, through w

11、hich the transient feature can be represented in the two-dimensional time–frequency plane. For example, the Wigner–Ville distribution (WVD) and improved Wigner–Ville distribution have been utilized to decompose vibration

12、 signals for fault diagnosis. It is no doubt that WVD has good concentration in the time–frequency plane. However, these methods are bilinear in nature, and there exist cross items in the decomposition results that can i

13、nterfere in</p><p>　　Even though some improved methods have been proposed, such as Choi–Willams distribution, cone-shaped distribution and so on, without exception, however, elimination of one shortcoming wi

14、ll always lead to the loss of other merits.</p><p>　　For example, the reduction of interference terms will bring the loss of time–frequency concentration. The wavelet transform, which is actually a kind of t

15、ime–frequency analysis method, provides the signal information in the time and the frequency domains simultaneously through a series of convolution operations between the signal being analyzed and the base wavelet under

16、different scaling parameters.</p><p>　　The application of the wavelet transform for mechanical fault diagnosis has been developed over the past decade . When detecting signal transients, the orthogonal wavel

17、et transform and the continuous wavelet transform are usually adopted. The orthogonal wavelet transform and multi- resolution analysis decompose the signal into orthogonal wavelet space.</p><p>　　The continu

18、ous wavelet transform can provide a finer scale resolution than orthogonal wavelet transform. It is more suitable for extracting mechanical fault information. Matching pursuit, a greedy algorithm that chooses at each ite

19、ration a waveform that is best adapted to approximate part of the signal, is effective in analyzing transient signals; however, the excessive computational cost limits its engineering applications.</p><p>　　

20、Correlation filtering, enlightened from matching pursuit, was introduced based on Laplace wavelet and applied to identify the parameters of transient by calculating the maximal correlation value, including the modal para

21、meters identification of a flutter for aerodynamic and structural testing , the natural frequency identification of a hydro- generator shaft and the wear fault diagnosis of the intake valve of an internal combustion engi

22、ne , detection the position and the depth for rotor crack .C</p><p>　　In this paper, considering periodic multiple transients, we propose a new technique, as an extension of LWCF but not limited to Laplace w

23、avelet, incorporating transient modeling and parameter identification for rotating machine fault diagnosis. Firstly, transient model is built to simulate a single transient and the parameters of model are identified. The

24、n the identified transient model and its parameters are used to construct periodic multi-transient model with the same time interval, i.e. peri</p><p><b>　　中文翻譯稿</b></p><p>　　旋轉機械以連續(xù)

25、的旋轉速度運動，局部的機械故障往往導致周期的沖擊，從而引起振動信號中的周期瞬變。瞬態(tài)特征分析對于局部故障檢測一直是問題的關鍵，瞬態(tài)特征分析的關鍵目標是確定瞬變模型及瞬變參數 （頻率、 阻尼比和時間索引） 以及瞬變之間的時間間隔?；谛〔ê拖嚓P濾波，建議納入瞬態(tài)建模和參數辨識技術用來對旋轉機械故障特征檢測。用所提出的方法，可以從振動信號中確定單一的瞬變和瞬變周期這兩個參數，可以基于這兩個參數，特別是基于周期檢測到局部的故障。第一，模擬信號

26、用于測試所提出的方法的性能。然后該方法分別應用于帶有局部故障的不同類型軸承的軸承外圈、 內圈和滾動體的振動信號，而所有結果都顯示描述局部故障特點的瞬變周期被成功檢測到。該方法也用于變速箱故障診斷和通過識別瞬態(tài)模型和周期這兩個參數進行有效性驗證。此外，可以繪制出軸承故障檢測，除了變速箱故障檢測適合雙側小波模型，單側小波模型比雙側小波模型更適合用這個方法。這項研究提出對于旋轉機械故障診斷的局部故障檢測通過瞬態(tài)建模和參數檢測是一種有效的方法。

27、</p><p>　　旋轉機械涵蓋范圍廣泛的機械設備，并在許多工業(yè)應用中，如飛機發(fā)動機、 傳動系統(tǒng)、 電廠等具有重要作用。機器的大部分被用在軸承、 齒輪箱及其他旋轉的部件這些可以導致機器故障。這些故障可能會導致機器癱瘓，造成重大經濟損失甚至災難性的人身傷亡。因此旋轉機械故障診斷的研究在過去幾十年里引起注意。瞬變或瞬態(tài)信號，特點是在很短的時間和很寬的頻率范圍上包含關于系統(tǒng)動力學研究的重要信息。例如，齒輪箱生成的振動

28、信號中的瞬變通常對應于局部故障的軸承或輪齒，如引起剝落、 裂紋、 破損和斷裂。因此，對于機故障診斷程序通過分析在振動信號中的瞬變描述機器正常的特點是非常有用的。</p><p>　　為了提取或分析振動信號的特征，尤其是瞬時的，在旋轉機器診斷文獻中不同的技術已經提出，如經驗模式分解 (EMD)、 獨立成分分析 (ICA)、 時間頻率表示（TFR) 、小波變換、匹配追蹤 (MP) 等。EMD，黃提出對于非線性和非平穩(wěn)

29、作為一種自適應分解技術，最近已擬定并廣泛應用于旋轉機械故障診斷，如齒輪故障診斷，滾動軸承故障診斷和轉子故障診斷。</p><p>　　EMD 分解成一組名為內在模式功能 （國際貨幣基金組織） 的完整和幾乎正交組件的復雜的信號。但是，它仍然有一些缺點。當談及計算瞬時頻率或在某些情況下，由于混合模式它可能揭示似是而非的特點，這一缺點使其在瞬態(tài)檢測和分析中的有效的應用站不住腳。ICA 被認為是盲源分離最有效的工具，同時

30、還被引入和應用于振動分析。ICA可以被看作是一個主成分分析（PCA）的延伸，旨在從沒有任何先驗知識集觀察瞬時線性混合的混合系統(tǒng)中恢復源信號。一些研究將ICA應用于提取振動信號的特征和檢測瞬變。雖然 ICA 在模擬信號的盲分離中是有效的，但是，由于復雜的原信號，并且信號在機械系統(tǒng)中的傳輸路徑是復雜多變的再加上噪聲污染，使得ICA在機械故障診斷中的有效應用仍處于發(fā)展初期。</p><p>　　TFR是最常用的方法，通

31、過它可以在時間頻率二維平面中表示瞬態(tài)特征。例如，維格納–分布（WVD）和改進的維格納–分布已被用于故障診斷中分解振動信號。毫無疑問WVD 在時間-頻率坐標系中很好的融合了。然而，這些方法本質上是雙線性的，這些并存的交叉項目在分析結果中會干擾特征的解釋。</p><p>　　即使一些改進的方法已提出，如Choi–Willams分布，錐形分布等，然而無一例外，伴隨著一個缺點的消除總會有其他優(yōu)點消失。</p>

32、;<p>　　例如，減少干擾項目將會帶來時間-頻率濃度的損失。小波變換，這實際上是一種時間–頻率分析方法，在時間和頻率領域提供信號信息的同時，在將要被分析的信號和不同尺度參數下的基本小波之間進行一系列的卷積運算。</p><p>　　小波變換在機械故障診斷中的應用已經有十年歷史。當檢測到信號瞬變時，通常采用正交小波變換和連續(xù)小波變換。正交小波變換和多分辨分析將信號分解成空間正交小波。</p&g

33、t;<p>　　連續(xù)小波變換可以提供一個更精細的分辨率比正交小波變換。連續(xù)小波變換更適合機械故障信息的提取。追蹤匹配，選擇一個很好應用于信號的近似部分的迭代波形進行貪婪算法在分析瞬變信號中是有效的；但是，計算成本過高限制了其工程應用。相關濾波，從追蹤匹配，推出基于拉普拉斯小波和通過計算最大相關性數值的應用識別瞬態(tài)參數，包括測試氣動和結構顫振模態(tài)參數識別、 水輪發(fā)電機組軸系固有頻率識別和磨損故障診斷的進水閥的內燃機檢測位置和

34、深度的轉子裂紋。拉普拉斯小波相關濾波 （LWCF）檢測單個瞬變是有效的，這在本質上只使用于作為小波瞬態(tài)模型的拉普拉斯小波和通過相關濾波確定的參數。然而，在旋轉的構件局部故障會導致周期性復雜瞬變，這可能與拉普拉斯小波不吻合，因此LWCF不適合所有在局部故障場合的瞬變模型。</p><p>　　本文中，考慮到周期性復雜瞬變，我們提出了一種新的技術，作為 LWCF 的擴展但不限于拉普拉斯小波，納入瞬態(tài)的旋轉機械故障診斷

35、的建模和參數辨識。首先，建立瞬態(tài)模型模擬單一瞬變并且標記模型的參數。然后識別的瞬態(tài)模型及參數用于構造具有相同時間間隔和周期的階段性復雜瞬變模型。最后，顯示旋轉機械故障的周期參數被確定。所提出的方法，并不限于識別單一瞬變但延長周期復雜小波和復雜瞬變。然后應用在典型的機械部件的故障診斷中。本文的其余部分按下面的順序組織。第 2 部分中介紹了關于瞬變模型和參數辨識的基本理論背景。第3部分通過仿真研究和分析，驗證了所提出的方法。第4部分講瞬態(tài)檢