外文翻譯---旋轉(zhuǎn)機(jī)械軸心軌跡識(shí)別方法研究

1、<p><b>　　附錄</b></p><p>　　Research of identification of shaft orbit for rotating machine</p><p>　　XIAO Sheng-guang</p><p>　?。═est Center of Chongqing University，Chon

2、gqing 400044，China）</p><p>　　Abstract：A novel approach for the identification of shaft orbit is presented. The vibration displacement signalsacquired in two mutually vertical directions were treated through

3、noise suppression and fitted to form a shaft orbit.Then the direction changing character was extracted and all shaft orbits were classified and identified with thefunction discriminated method according to the pattern re

4、cognition theory. Each type of shaft orbit was described indetail with one character，which can help to</p><p>　　Key words：Shaft orbits；Fault diagnosis；Geometric features；Pattern recognition；Thinning classifi

5、cation</p><p>　　1 The introduction</p><p>　　With the development of science and technology and modern industry, to rotating machines the large-scale, high-speed and automation direction, the sha

6、pe of rotating machinery state monitoring and fault diagnosis is put forward higher request, the axis trajectory for rotating machinery is an important state characteristic parameters, can be simple and straight view, vi

7、vidly reflect the running status of equipment. Through to the axis of track observation, can determine some of the common faults, </p><p>　　Axis path at present, already have several identification methods,

8、including [1-2] invariant moment method, a two-dimensional image gray level matrix [3]. literature [1-2] axis path with seven moment invariants as feature vectors, recognition by the distance between the characteristics

9、of axial trajectory shape, literature [3] the axis trajectory image coding, using neural network for identification. Both methods can better identify axis path, but the method is complex, relatively large amount o</p&

10、gt;<p>　　2 Axis locus corresponding fault mechanism analysis</p><p>　　Axis path refers to the axis on a bit relative to the trajectory of the bearing, the trajectory is in a plane perpendicular to the

11、 axis, so it requires the setting sensors in both directions in the plane. Axis path clearly describes the fault characteristics of implication in the unit, the axis trajectory can get in on the rotor bending, imbalance,

12、 instability and dynamic-static friction bearing and other information. Through the actual operation of rotating machinery fault mechanism analysis an</p><p>　　3 Image processing axis path recognition princi

13、ple [4]</p><p>　　In image recognition, is the simplest method of identification for template matching. Is the unknown image compared to a standard image, see whether they are the same or similar.</p>

14、<p>　　Has M category: 1 omega, omega 1,... , M each type of feature vector by a number of omega said, such as class I class, omega has:</p><p>　　Xi = [xi1, xi2, xi3,..., xin] T</p><p>　　For

15、 any identified trajectory image X:</p><p>　　X = [x1, x2, x3,..., xn] T</p><p>　　Calculate distance d (Xi, X), if there is one, I made:</p><p>　　d (Xi, X) < d (Xj, X), j = 1, 2,.

16、.., M, I indicates j ∈ X omega I.</p><p>　　Specific discriminant, X, Y distance two points can be used | X, Y | 2</p><p>　　Said, namely:</p><p>　　d (X, Xi) - Xi | = | X 2 = (X - Xi)

17、 T = (X - Xi)</p><p>　　XTX XTXT - XiTXi =XTX - (XTXT + XiTX - XiTXi)</p><p>　　Type of XTXT + XiTX - characterized XiTXi linear function, can be used as discriminant function:</p><p&g

18、t;　　di (X) = XTXT + XiTX – XiTXi.</p><p>　　If d (X, Xi) = min {di (X)}, then X ∈ omega I. This is the kind of problem, the minimum distance criterion. In this paper, the axis path identification in this way.

19、</p><p>　　4 Axis trajectory image feature extraction and recognition</p><p>　　Axis path can be used to identify the image feature has a lot of, now use more features are: invariant moment[1], th

20、e cross points, circle number, center of mass position, curvature, length, etc. Based on the direction of the axis trajectory change as the main characteristics, and some other features are realized tracing above.</p&

21、gt;<p>　　4.1 Axis trajectory image preprocessing [5]</p><p>　　Acquisition of two way data before the synthesis has been underway for filtering de-noising treatment, eliminate a lot of burr. </p>

22、<p>　　Figure1 The conditions of deleting</p><p>　　As shown in figure 1, axis path line is at an Angle, was on the way to draw black spots position should be in the path, but considering that in order

23、not to make the direction changing, change to figure this is on the corner points, should be deleted (corresponding to the four kinds of situations), delete the conditions are:</p><p>　　| x [I + 2] - [I] x |

24、 | = 1 and y [I + 2] - [I] y | = 1</p><p>　　If meet the above conditions, the delete (x + 1], [I, y [I + 1].</p><p>　　4.2 Feature extraction and quantification of [5]</p><p>　　To qu

25、antify characteristics, specifies the following four directions: to the right, down, left, up (in the program can be expressed in Numbers or corresponding bits, this paper use Numbers 1, 2, 3, 4), contains the scope of t

26、he direction as shown in figure 2.</p><p>　　Figure2 Stability in the direction of the range</p><p>　　Was three scope are included in each direction, is to avoid a small perturbation to change di

27、rection, you can see from the above four, four direction on the diagonal lines, each containing in two directions, to determine the direction of, have the following rules:</p><p>　　(1) for each starting poin

28、t, when the shaft rotates clockwise, to choose direction priority sequence is to the right, down, left, up, and the corresponding number is 1, 2, 3, 4; When the shaft rotates counterclockwise, to choose direction priorit

29、y sequence is to the left, down, right, up, the corresponding number is 1, 2, 3, 4. Such axis path is to work in the same state, which is formed by the different direction of rotation of the characteristic value.</p&g

30、t;<p>　　(2) if you have in one direction, so in one direction, then should keep and original in the same direction as far as possible, so that the direction fluctuation in a small scope, can be aligned, unless hav

31、e jumped from the direction of scope, which is to avoid the characteristics of the two adjacent to the opposite direction.</p><p>　　After got the direction sequence, to assist in the description, also can ca

32、lculate some feature such as number of intersection point, end point, the distance to the intersection first point from the intersection point of distance, etc., these features also use numerals, this paper selects the n

33、ode number to describe.</p><p>　　4.3 The classification of axis path description</p><p>　　Using the above methods can be classified on the axis trajectory graphics recognition, but belong to the

34、 same kind of classification of the two graphics, shape may also have very big difference. In order to understand the severity of the failure, and other characteristics to measure the size of the track deformation.</p

35、><p>　　4.3.1 Unbalanced fault</p><p>　　Axis trajectory for the oval, graphic long axis and short axis L L, the ratio of their C = L/L is fine length [6], C axis path can represent the size of the d

36、eformation degree. Due to the direction of the circle and ellipse feature vector is the same, in C can also be used to distinguish whether there is a fault. 0 C or less or less1, C is smaller, the elliptical deformation

37、degree, the greater the failure, C = 1 indicates no fault.</p><p>　　Figure3 Length of the thin</p><p>　　4.3.2 Imbalance and comprehensive fault in the wrong</p><p>　　Axis trajectory

38、 graphics for banana fan, its deformation characteristics can be expressed in its bending degree. To take the first axis trajectory of the center of mass. According to the physical concept of center of gravity, define th

39、e two-dimensional gray-scale image centroid is as follows:</p><p>　　Find two corner point axis path, become card axis of connections between them with the center of mass. , finding the Angle between the two

40、card axis AArg define AArg for bending. 0 or less AArg PI or less, the smaller AArg, said graphics completely, the greater the degree of the failure is more serious.</p><p>　　4.3.3 Misalignment and oil film

41、vortex breakdown</p><p>　　Axis trajectory is figure 8 and figure 8, respectively the distinction of the two tracks is have a intersection point. Find trajectory intersection to intersection point as segmenta

42、tion point, the original sequence is divided into two parts. Respectively in the area of the two parts of S1, S2. The area ratio of two ring is:</p><p><b>　　C1=</b></p><p>　　Where 0

43、< C1 is 1 or less, the size of C1 unstable factors in the reaction the rotation axis of strong or weak, C1, said the greater the role played by the unstable factors.</p><p>　　5 The simulation research<

44、/p><p>　　For each categories of axis path, select a representative which can identify four kinds of computing. The result is shown in figure 4.</p><p>　　Figure4 The axis trajectory simulation</p

45、><p>　　Axis of the calculation result shows that each categories of trajectory eigenvalues were extracted by different, use criterion can easily draw categories to which they belong, to judge fault in rotating

46、machinery. By detailed description of parameters of the calculation result shows that belong to the same categories of axis trajectory, the shape also has the very big difference, refinement parameters can well said this

47、 kind of difference, help us to judge the severity of the fault.</p><p>　　6 conclusion</p><p>　　Axis path based on a number of engaged in automatic identification research results, the scholars

48、in the direction of the direction of quantitative change characteristics, combined with the other characteristics, to build into a template, then use the theory of pattern recognition to identify, for the axis trajectory

49、 automatic identification provides a new method.</p><p>　　References</p><p>　　1 Thousands of xiuzhou district, Li Yonggang Li Heming. Based on moment invariant features and the new automatic axi

50、s trajectory shape correlation recognition [J]. Journal of engineering for thermal energy and power, 2005, 20 (3) : 239-241.</p><p>　　2 NiChuanKun Zhou Jianzhong, FuBo. Based on the improved moment invariant

51、 generator axis trajectory recognition [J]. Electric power science and engineering, 2004 (3) : 16-19.</p><p>　　3 Professor. Axis locus and automatic recognition for the purification of research [J]. Journal

52、of wuhan university of technology, transportation science and engineering edition, 2003, 27 (6) : 878-881.</p><p>　　4 Yang Shuying. Image pattern recognition [M]. Beijing: tsinghua university press, 2005.<

53、;/p><p>　　5 Zhang Honglin. Visual c + + digital image pattern recognition technology and engineering practice [M]. Beijing: people's posts and telecommunications press, 2003.</p><p>　　6 Jiang Z

54、hinong Li Yanni. Rotating machinery axis trajectory feature extraction technology research [J]. Journal of vibration and the test and diagnosis, 2007, 27 (2) : 98-102.</p><p>　　旋轉(zhuǎn)機(jī)械軸心軌跡識(shí)別方法研究</p><

55、p><b>　　肖圣光</b></p><p>　　(重慶大學(xué)測(cè)試中心，重慶400044)</p><p>　　摘要：提出了一種識(shí)別軸心軌跡的新方法。采集方向相互垂直的兩路振動(dòng)位移信號(hào)，經(jīng)消噪處理后擬合為軸心軌跡，提取軸心軌跡的方向變化特征，利用模式識(shí)別理論中的函數(shù)判別法進(jìn)行分類識(shí)別。并對(duì)每種類別的軸心軌跡，用一個(gè)特征參量來(lái)進(jìn)行細(xì)化描述，不僅可以判斷機(jī)械的運(yùn)行狀

56、態(tài)，在發(fā)生故障的時(shí)候還能對(duì)故障嚴(yán)重程度進(jìn)行評(píng)估。通過(guò)對(duì)仿真分析，取得了良好效果。</p><p>　　關(guān)鍵詞：軸心軌跡；故障診斷；幾何特征；模式識(shí)別；細(xì)化分類</p><p><b>　　1 引言</b></p><p>　　隨著科學(xué)技術(shù)和現(xiàn)代工業(yè)的發(fā)展，旋轉(zhuǎn)機(jī)械向著大型、高速和自動(dòng)化方向發(fā)展，這對(duì)旋轉(zhuǎn)機(jī)械狀態(tài)監(jiān)測(cè)和故障診斷提出了更高的要求，軸

57、心軌跡作為旋轉(zhuǎn)機(jī)械的一個(gè)重要的狀態(tài)特征參量，能簡(jiǎn)單、直觀、形象地反映設(shè)備的運(yùn)行狀況。通過(guò)對(duì)軸心軌跡的觀察，可以判斷出一些常見(jiàn)的故障，例如油膜渦動(dòng)、油膜振蕩、軸不對(duì)中等。傳統(tǒng)的軸心軌跡形狀和動(dòng)態(tài)特性的識(shí)別是基于人機(jī)對(duì)話模式實(shí)現(xiàn)的，嚴(yán)重影響了故障診斷的智能化水平。為了提高故障診斷的智能化程度，需要深入研究旋轉(zhuǎn)機(jī)械的軸心軌跡自動(dòng)識(shí)別技術(shù)。</p><p>　　目前，已經(jīng)有了幾種軸心軌跡識(shí)別方法，其中包括不變矩法[1-2

58、]，二維圖像灰度矩陣[3]。文獻(xiàn)[1-2]用軸心軌跡的7 個(gè)不變矩作為特征向量，通過(guò)特征量之間的距離來(lái)識(shí)別軸心軌跡形狀，文獻(xiàn)[3]將軸心軌跡圖象進(jìn)行編碼，利用神經(jīng)網(wǎng)絡(luò)進(jìn)行識(shí)別。這兩種方法都能較好的識(shí)別軸心軌跡，但是方法復(fù)雜，計(jì)算量比較大。在總結(jié)前人工作的基礎(chǔ)上，針對(duì)軸心軌跡本身的變化特點(diǎn)，提出了一種新的識(shí)別方法，通過(guò)提取軸心軌跡一個(gè)周期的方向變化特征來(lái)進(jìn)行分類識(shí)別，并對(duì)每種類別的軸心軌跡，提出一種能，來(lái)細(xì)化描述其變形程度的參量，進(jìn)一步了

59、解故障的嚴(yán)重程度，而且特征提取速度快，效率高。</p><p>　　2 軸心軌跡對(duì)應(yīng)的軸承故障機(jī)理分析</p><p>　　軸心軌跡是指軸心上一點(diǎn)相對(duì)于軸承座的運(yùn)動(dòng)軌跡，這一軌跡是在與軸線垂直的平面內(nèi)，因此它要求在該平面內(nèi)兩個(gè)方向上設(shè)置傳感器。軸心軌跡清晰地描述了蘊(yùn)涵在機(jī)組內(nèi)的故障特征，軸心軌跡中可以獲取有關(guān)轉(zhuǎn)子彎曲、不平衡、軸瓦失穩(wěn)和動(dòng)靜摩擦等信息。通過(guò)對(duì)實(shí)際運(yùn)行的旋轉(zhuǎn)機(jī)械故障機(jī)理的分析

60、和大量理論分析，人們總結(jié)出幾種軸心軌跡所對(duì)應(yīng)的故障集。</p><p>　　實(shí)際采樣的信號(hào)并不是一個(gè)整周期的，所以需要將其按照最大周期分量對(duì)采樣數(shù)據(jù)進(jìn)行截取，取得一個(gè)整周期的封閉曲線。將采集到的信號(hào)進(jìn)行提純，合成后，存儲(chǔ)到一個(gè)表示x，y 坐標(biāo)的坐標(biāo)序列中：{x（n），y（n）：n=0，1，…，N-1}，通過(guò)分析這個(gè)序列中x、y 變化的特征來(lái)識(shí)別軸心軌跡。</p><p>　　3 圖像處理識(shí)

61、別軸心軌跡的原理[4]</p><p>　　在圖像識(shí)別中，最簡(jiǎn)單的識(shí)別方法就是模板匹配。就是把未知圖像和一個(gè)標(biāo)準(zhǔn)圖像相比，看它們是否相同或相似。</p><p>　　設(shè)有M 個(gè)類別：ω1，ω1，…，ωM 每類特征由若干個(gè)向量表示，如類ωi 類，有：</p><p>　　Xi=[xi1，xi2，xi3，…，xin]T</p><p>　　對(duì)于任

62、意被識(shí)別的軌跡圖像X：</p><p>　　X = [x1, x2, x3,..., xn] T</p><p>　　計(jì)算距離d（Xi，X），若存在某一個(gè)i，使：</p><p>　　d（Xi，X）＜d（Xj，X），j=1，2，…，M，i≠j （3）</p><p><b>　　則X∈ωi。</b></p>

63、<p>　　具體判別的時(shí)候，X，Y 兩點(diǎn)距離可以用|X，Y|2表示，即：</p><p>　　d (X, Xi)= Xi- X 2 = (X - Xi) T (X - Xi)</p><p>　　XTX-XTXT - XiTXi =XTX - (XTXT + XiTX - XiTXi)</p><p>　　式中的XTXT+XiTX-XiTXi 為特征的

64、線性函數(shù)，可作為判別函數(shù)：</p><p>　　di(X)=XTXT+XiTX-XiTXi</p><p>　　若d(X，Xi)=min{di(X)}，則X∈ωi。這就是多類問(wèn)題的最小距離判別法。本文就用這種方法識(shí)別軸心軌跡。</p><p>　　4 軸心軌跡圖像特征的提取和識(shí)別</p><p>　　軸心軌跡圖像特征的提取和識(shí)別可以用來(lái)識(shí)別軸

65、心軌跡圖像的特征有很多，目前利用較多的特征有：不變矩[1]，交叉點(diǎn)數(shù)，圓環(huán)數(shù)，質(zhì)心位置，彎曲度，細(xì)長(zhǎng)度等。本文以軸心軌跡的方向變化為主要特征，并用一些其他特征進(jìn)行細(xì)化描述。</p><p>　　4.1 軸心軌跡圖像的預(yù)處理[5]</p><p>　　采集的兩路數(shù)據(jù)在合成前已經(jīng)進(jìn)行了濾波消噪處理，消除掉了很多毛刺。但是為了后面特征提取的方便以及減少數(shù)據(jù)量，還需要做一些預(yù)處理。</p&g

66、t;<p><b>　　圖1 刪除條件</b></p><p>　　如圖1，斜著的直線是軸心軌跡，本來(lái)途中畫黑點(diǎn)的位置都應(yīng)該在路徑里的，但考慮到為了不使方向變來(lái)變?nèi)?，?duì)于改圖這種處于拐角上的點(diǎn)，都要?jiǎng)h除掉（相對(duì)應(yīng)的有4 種情況），刪除的條件是：|x[I+2]-x[I]|=1 且|y[I+2]-y[I]|=1。 如果滿足以上條件，則刪除（x[I+1]，y[I+1]）點(diǎn)。</

67、p><p>　　4.2 特征的提取與量化</p><p>　　為了量化特征，規(guī)定了如下4 個(gè)方向：向右，向下，向左，向上(在程序中可以用數(shù)字或相應(yīng)的比特位表示，本文用數(shù)字1，2，3，4 來(lái)表示)，各方向包含的范圍見(jiàn)圖2。</p><p>　　圖2 穩(wěn)定的方向范圍</p><p>　　之所以每個(gè)方向都包含3 個(gè)范圍，是為了避免一些小的擾動(dòng)改變方向，

68、從上面4 個(gè)圖中可以看到，在斜線上的4 個(gè)方向，每一個(gè)都包含在兩個(gè)方向中，對(duì)于方向的確定，有如下規(guī)則：</p><p>　?。?）對(duì)于每一個(gè)起點(diǎn)，當(dāng)軸順時(shí)針旋轉(zhuǎn)時(shí)，選擇方向的優(yōu)先順序依次是向右、向下，向左，向上，對(duì)應(yīng)的數(shù)字是1，2，3，4；當(dāng)軸逆時(shí)針旋轉(zhuǎn)時(shí)，選擇方向的優(yōu)先順序依次是向左，向下，向右，向上，對(duì)應(yīng)數(shù)字是1，2，3，4。這樣是為了軸心軌跡在同樣的工作狀態(tài)，不同的旋轉(zhuǎn)方向所形成的特征值一樣。</p&

69、gt;<p>　?。?）如果已經(jīng)處在一個(gè)方向，那么對(duì)于接著的一個(gè)方向，應(yīng)盡量保持和原來(lái)的方向一致，這樣方向在一個(gè)小范圍內(nèi)波動(dòng)，可以保持一致，除非已經(jīng)跳離了這個(gè)方向所在的范圍，也就是盡量避免兩個(gè)相鄰的特征為相反方向。</p><p>　　在得到了方向序列后，還可以計(jì)算一些特征量來(lái)輔助描述，如交點(diǎn)個(gè)數(shù)、尾點(diǎn)到交點(diǎn)的距離、首點(diǎn)距交點(diǎn)的距離等，這些特征也用數(shù)字來(lái)表示，本文選用交點(diǎn)個(gè)數(shù)來(lái)描述。</p&g

70、t;<p>　　4.3 軸心軌跡的分類描述</p><p>　　用上面的方法可以對(duì)軸心軌跡圖形進(jìn)行分類識(shí)別，但是屬于同一種分類的兩個(gè)圖形，形狀也可能有很大的差別。為了清楚的了解故障的嚴(yán)重程度，還要用其他特征量來(lái)衡量軌跡變形量的大小。</p><p>　　4.3.1 不平衡故障</p><p>　　軸心軌跡為橢圓形，求得圖形的長(zhǎng)軸L 和短軸l，它們的比值

71、C=l/L 為細(xì)長(zhǎng)度[6]，用C 可以表示軸心軌跡變形程度的大小。由于圓和橢圓的特征向量相同，用C 還可以用來(lái)判別是否有故障。0≤C≤1，C 越小，橢圓變形程度越大，故障越嚴(yán)重，C=1 時(shí)表示沒(méi)有故障。</p><p><b>　　圖3 細(xì)長(zhǎng)度</b></p><p>　　4.3.2 不平衡與不對(duì)中綜合故障</p><p>　　曲程度來(lái)表示。先

72、求出軸心軌跡的質(zhì)心。根據(jù)物理上重心的概念，定義二維灰度圖像的質(zhì)心如下：</p><p>　　找到軸心軌跡的兩個(gè)拐角點(diǎn)，它們與質(zhì)心之間的連線成為卡軸。求得兩個(gè)卡軸之間的夾角AArg，定義AArg 為彎曲度。0≤AArg≤pi，AArg 越小，表示圖形的完全程度越大，故障越嚴(yán)重。</p><p>　　4.4.3 不對(duì)中和油膜渦動(dòng)故障</p><p>　　軸心軌跡分別為外

73、8 字型和內(nèi)8 字形，這兩種軌跡的特點(diǎn)就是有一個(gè)交點(diǎn)。找到軌跡的交點(diǎn)，以交點(diǎn)為分割點(diǎn)，將原序列分成兩部分。分別求處兩部分的面積S1，S2。兩個(gè)環(huán)形的面積比:</p><p><b>　　C1=</b></p><p>　　其中0＜C1≤1，C1 的大小反應(yīng)了軸系旋轉(zhuǎn)中不穩(wěn)定因素的強(qiáng)勢(shì)或弱勢(shì)，C1 越大，表示不穩(wěn)定因素所起作用越大。</p><p&g

74、t;<b>　　5 仿真研究</b></p><p>　　對(duì)每種類別的軸心軌跡，選取有代表性的4 種進(jìn)行識(shí)別計(jì)算。得到的結(jié)果如圖4。</p><p><b>　　圖4 軸心軌跡仿真</b></p><p>　　由計(jì)算結(jié)果可知，每種類別的軸心軌跡所提取出來(lái)的特征值都不同，用判別法很容易就可以得出它們所屬的類別，從而判斷旋轉(zhuǎn)機(jī)

75、械所發(fā)生的故障。由細(xì)化描述參量的計(jì)算結(jié)果可知，屬于同種類別的軸心軌跡，其形狀也有很大的差別，細(xì)化參量可以很好的表示這種差別，幫助我們判斷故障的嚴(yán)重程度。</p><p><b>　　6 結(jié)束語(yǔ)</b></p><p>　　向的學(xué)者的成果，將方向變化特征量化，結(jié)合其它特征，構(gòu)建成模板，然后用模式識(shí)別的理論進(jìn)行識(shí)別，為軸心軌跡的自動(dòng)識(shí)別提供了新的方法。</p>

76、<p><b>　　參考文獻(xiàn)</b></p><p>　　[1] 萬(wàn)書亭，李永剛，李和明.基于不變矩特征和新型關(guān)聯(lián)度的軸心軌跡形狀自動(dòng)識(shí)別[J].熱能動(dòng)力工程，2005，20（3）：239-241.</p><p>　　[2] 倪傳坤，周建中，付波.基于改進(jìn)不變矩的發(fā)電機(jī)組軸心軌跡識(shí)別[J].電力科學(xué)與工程，2004，16（3）：16-19.</p

77、><p>　　[3] 陳豫.軸心軌跡提純與自動(dòng)識(shí)別的研究[J].武漢理工大學(xué)學(xué)報(bào)：交通科學(xué)與工程版，2003，27（6）：878-881.</p><p>　　[4] 楊淑瑩.圖像模式識(shí)別[M].北京：清華大學(xué)出版社，2005.</p><p>　　[5] 張宏林.Visual C++數(shù)字圖像模式識(shí)別技術(shù)及工程實(shí)踐[M].北京：人民郵電出版社，2003.</p&g