外文翻译---旋转机械轴心轨迹识别方法研究.docx

附录Research of identification of shaft orbit for rotating machineXIAO Sheng-guang（Test Center of Chongqing University，Chongqing 400044，China）Abstract：A novel approach for the identification of shaft orbit is presented. The vibration displacement signalsacquired in two mutually vertical directions were treated through 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 recognition theory. Each type of shaft orbit was described indetail with one character，which can help to judge the operation status of the mechanical and the extent of thefailure. The analysis and simulation got good results.Key words：Shaft orbits；Fault diagnosis；Geometric features；Pattern recognition；Thinning classification1 The introductionWith the development of science and technology and modern industry, to rotating machines the large-scale, high-speed and automation direction, the shape 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, vividly reflect the running status of equipment. Through to the axis of track observation, can determine some of the common faults, such as oil film vortexStill, oil film oscillation, shaft not medium. The traditional axis locus and shape the dynamic characteristics identification is based on the man-machine dialogue mode, serious affect the level of intelligent fault diagnosis. In order to improve the degree of intelligent fault diagnosis, it is necessary to in-depth study the trajectory of the axis of rotating machinery automatic identification technology.Axis path at present, already have several identification methods, 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 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 of calculation. On the basis of summarizing predecessors work, according to the characteristics of the axis trajectory itself changes, this paper proposes a new recognition method, by extracting a cycle in the direction of the axis of track change features for classification, and for each categories of axis trajectory, put forward a kind of ability, refine to describe the deformation degree of parameters, further understand the severity of the fault, and feature extraction speed, high efficiency.2 Axis locus corresponding fault mechanism analysisAxis path refers to the axis on a bit relative to the trajectory of the bearing, the trajectory is in a plane perpendicular to the 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, instability and dynamic-static friction bearing and other information. Through the actual operation of rotating machinery fault mechanism analysis and theoretical analysis, it sums up the axis of some typical trajectories of the fault. Actual sampling of the signal is not a whole cycle, so needs to be carried out in accordance with the maximum cycle component to sampling data interception, make one complete cycle of the closed curve. In the collected signal is: x (n), y (n) : n = 0, 1,. , N - 1, through the analysis of a sequence of change characteristics of x, y axis path to identify.3 Image processing axis path recognition principle 4In 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.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:Xi = xi1, xi2, xi3,., xin TFor any identified trajectory image X:X = x1, x2, x3,., xn TCalculate distance d (Xi, X), if there is one, I made:d (Xi, X) d (Xj, X), j = 1, 2,., M, I indicates j X omega I.Specific discriminant, X, Y distance two points can be used | X, Y | 2Said, namely:d (X, Xi) - Xi | = | X 2 = (X - Xi) T = (X - Xi)XTX XTXT - XiTXi =XTX - (XTXT + XiTX - XiTXi)Type of XTXT + XiTX - characterized XiTXi linear function, can be used as discriminant function:di (X) = XTXT + XiTX XiTXi.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.4 Axis trajectory image feature extraction and recognitionAxis path can be used to identify the image feature has a lot of, now use more features are: invariant moment1, the 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.4.1 Axis trajectory image preprocessing 5Acquisition of two way data before the synthesis has been underway for filtering de-noising treatment, eliminate a lot of burr. Figure1 The conditions of deletingAs 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 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:| x I + 2 - I x | | = 1 and y I + 2 - I y | = 1If meet the above conditions, the delete (x + 1, I, y I + 1.4.2 Feature extraction and quantification of 5To quantify 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 the direction as shown in figure 2.Figure2 Stability in the direction of the rangeWas three scope are included in each direction, is to avoid a small perturbation to change direction, 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:(1) for each starting point, 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 priority 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.(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 have jumped from the direction of scope, which is to avoid the characteristics of the two adjacent to the opposite direction.After got the direction sequence, to assist in the description, also can calculate 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 node number to describe.4.3 The classification of axis path descriptionUsing the above methods can be classified on the axis trajectory graphics recognition, but belong to the 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.4.3.1 Unbalanced faultAxis 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 deformation 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 degree, the greater the failure, C = 1 indicates no fault.Figure3 Length of the thin4.3.2 Imbalance and comprehensive fault in the wrongAxis trajectory 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 the two-dimensional gray-scale image centroid is as follows:Find two corner point axis path, become card axis of connections between them with the center of mass. , finding the Angle between the two 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.4.3.3 Misalignment and oil film vortex breakdownAxis 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 segmentation 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:C1=Where 0 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.5 The simulation researchFor each categories of axis path, select a representative which can identify four kinds of computing. The result is shown in figure 4.Figure4 The axis trajectory simulationAxis 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 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 kind of difference, help us to judge the severity of the fault.6 conclusionAxis path based on a number of engaged in automatic identification research results, the scholars 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 automatic identification provides a new method.References1 Thousands of xiuzhou district, Li Yonggang Li Heming. Based on moment invariant features and the new automatic axis trajectory shape correlation recognition J. Journal of engineering for thermal energy and power, 2005, 20 (3) : 239-241.2 NiChuanKun Zhou Jianzhong, FuBo. Based on the improved moment invariant generator axis trajectory recognition J. Electric power science and engineering, 2004 (3) : 16-19.3 Professor. Axis locus and automatic recognition for the purification of research J. Journal of wuhan university of technology, transportation science and engineering edition, 2003, 27 (6) : 878-881.4 Yang Shuying. Image pattern recognition M. Beijing: tsinghua university press, 2005.5 Zhang Honglin. Visual c + + digital image pattern recognition technology and engineering practice M. Beijing: peoples posts and telecommunications press, 2003.6 Jiang Zhinong Li Yanni. Rotating machinery axis trajectory feature extraction technology research J. Journal of vibration and the test and diagnosis, 2007, 27 (2) : 98-102.旋转机械轴心轨迹识别方法研究肖圣光(重庆大学测试中心，重庆400044)摘要：提出了一种识别轴心轨迹的新方法。采集方向相互垂直的两路振动位移信号，经消噪处理后拟合为轴心轨迹，提取轴心轨迹的方向变化特征，利用模式识别理论中的函数判别法进行分类识别。并对每种类别的轴心轨迹，用一个特征参量来进行细化描述，不仅可以判断机械的运行状态，在发生故障的时候还能对故障严重程度进行评估。通过对仿真分析，取得了良好效果。关键词：轴心轨迹；故障诊断；几何特征；模式识别；细化分类1 引言随着科学技术和现代工业的发展，旋转机械向着大型、高速和自动化方向发展，这对旋转机械状态监测和故障诊断提出了更高的要求，轴心轨迹作为旋转机械的一个重要的状态特征参量，能简单、直观、形象地反映设备的运行状况。通过对轴心轨迹的观察，可以判断出一些常见的故障，例如油膜涡动、油膜振荡、轴不对中等。传统的轴心轨迹形状和动态特性的识别是基于人机对话模式实现的，严重影响了故障诊断的智能化水平。为了提高故障诊断的智能化程度，需要深入研究旋转机械的轴心轨迹自动识别技术。目前，已经有了几种轴心轨迹识别方法，其中包括不变矩法1-2，二维图像灰度矩阵3。文献1-2用轴心轨迹的7 个不变矩作为特征向量，通过特征量之间的距离来识别轴心轨迹形状，文献3将轴心轨迹图象进行编码，利用神经网络进行识别。这两种方法都能较好的识别轴心轨迹，但是方法复杂，计算量比较大。在总结前人工作的基础上，针对轴心轨迹本身的变化特点，提出了一种新的识别方法，通过提取轴心轨迹一个周期的方向变化特征来进行分类识别，并对每种类别的轴心轨迹，提出一种能，来细化描述其变形程度的参量，进一步了解故障的严重程度，而且特征提取速度快，效率高。2 轴心轨迹对应的轴承故障机理分析轴心轨迹是指轴心上一点相对于轴承座的运动轨迹，这一轨迹是在与轴线垂直的平面内，因此它要求在该平面内两个方向上设置传感器。轴心轨迹清晰地描述了蕴涵在机组内的故障特征，轴心轨迹中可以获取有关转子弯曲、不平衡、轴瓦失稳和动静摩擦等信息。通过对实际运行的旋转机械故障机理的分析和大量理论分析，人们总结出几种轴心轨迹所对应的故障集。实际采样的信号并不是一个整周期的，所以需要将其按照最大周期分量对采样数据进行截取，取得一个整周期的封闭曲线。将采集到的信号进行提纯，合成后，存储到一个表示x，y 坐标的坐标序列中：x（n），y（n）：n=0，1，N-1，通过分析这个序列中x、y 变化的特征来识别轴心轨迹。3 图像处理识别轴心轨迹的原理4在图像识别中，最简单的识别方法就是模板匹配。就是把未知图像和一个标准图像相比，看它们是否相同或相似。设有M 个类别：1，1，M 每类特征由若干个向量表示，如类i 类，有：Xi=xi1，xi2，xi3，xinT对于任意被识别的轨迹图像X：X = x1, x2, x3,., xn T计算距离d（Xi，X），若存在某一个i，使：d（Xi，X）d（Xj，X），j=1，2，M，ij （3）则Xi。具体判别的时候，X，Y 两点距离可以用|X，Y|2表示，即：d (X, Xi)= Xi- X 2 = (X - Xi) T (X - Xi)XTX-XTXT - XiTXi =XTX - (XTXT + XiTX - XiTXi)式中的XTXT+XiTX-XiTXi 为特征的线性函数，可作为判别函数：di(X)=XTXT+XiTX-XiTXi若d(X，Xi)=mindi(X)，则Xi。这就是多类问题的最小距离判别法。本文就用这种方法识别轴心轨迹。4 轴心轨迹图像特征的提取和识别轴心轨迹图像特征的提取和识别可以用来识别轴心轨迹图像的特征有很多，目前利用较多的特征有：不变矩1，交叉点数，圆环数，质心位置，弯曲度，细长度等。本文以轴心轨迹的方向变化为主要特征，并用一些其他特征进行细化描述。4.1 轴心轨迹图像的预处理5采集的两路数据在合成前已经进行了滤波消噪处理，消除掉了很多毛刺。但是为了后面特征提取的方便以及减少数据量，还需要做一些预处理。图1 删除条件如图1，斜着的直线是轴心轨迹，本来途中画黑点的位置都应该在路径里的，但考虑到为了不使方向变来变去，对于改图这种处于拐角上的点，都要删除掉（相对应的有4 种情况），删除的条件是：|xI+2-xI|=1 且|yI+2-yI|=1。 如果满足以上条件，则删除（xI+1，yI+1）点。4.2 特征的提取与量化为了量化特征，规定了如下4 个方向：向右，向下，向左，向上(在程序中可以用数字或相应的比特位表示，本文用数字1，2，3，4 来表示)，各方向包含的范围见图2。图2 稳定的方向范围之所