UltrasonicIn_lin_省略_Multi_classifier_XUY

Ultrasonic In-line Inspection of Pipeline Corrosion Based onSupport Vector Machine Multi-classifier *XU Yun 1,2, DAI Bo 2, TIAN Xiaoping 2 , SHENG Sha 21. Beijing University of Chemical Technology , Beijing 100029, P.R.ChinaE-mail: xuyun@2. Beijing Institute of Petro-chemical Technology, Beijing 102617, P.R.ChinaE-mail: daibo@Abstract ：Because of the complicated condition in pipeline, ultrasonic detection echoes are affected by many factors such as the line defect orientation, the defect width, the branching-point geometry, wall roughness, working state and the interaction between the different echoes. So it is difficult to distinguish ultrasonic detection echoes. Analyze ultrasonic detection echoes from experiments, use radial basis kernel function to process the signal, and achieve automatic recognition by using one-against-rest algorithm and tree algorithm. The experimental results show that the method not only improves the recognition correct rate, but also improves the computing speed, and achieves good results. Key Words ：Ultrasonic Inspection, Support Vector Machine, Multi-classifier*This work is supported by China Petroleum & Chemical Corporation Science Foundation under Grant J305011.1 INTRODUCTIONWith the development of oil and gas fields, more and more attention is paid to pipelines safe operation. Long-distance pipeline inspection has become urgent to Oil and Gas Storage and Transportation Department. At present, the detection technology of long-distance pipelines is mostly using traditional outside inspection in china, such as the pipeline corrosion is inspected by the catholic protection system. Although this method can detect buried pipeline not only without excavation but also without affecting the normal work, yet it belongs to an indirect method of detection of pipeline corrosion, and the raw data often require careful analysis and check. At the same time, it can not detect the pipeline buried under road, rail, marine and other areas, and also can not be achieved the overall detection [1]. For the problems of outside inspection, some developed countries have developed a number of possible pipeline corrosion detection technologies. With advantages that can detect a greater thickness of material with high-speed, a penetration ability, real-time display, light automation equipment, ultrasonic inspection has developed into a very important non-destructive method, and has been widely used in production practice. Ultrasonic in-line inspection, based on the principle of multi-channel ultrasonic automated inspection, adopts a detector with probes arrayed in a circle to scan and inspect corrosion. Because of the complicated condition in pipeline, ultrasonic detection echoes contain a lot of information about defects and also are mixed with various kinds of interference noise. The noise will result in errors in the signal-processing even cause signals to be submerged [2]. Echo signal processing is the key technology to ultrasonic in-line inspection. Ultrasonic echo signal processing methods mainly include statistical analysis, spectral analysis, fuzzy logic, wavelet analysis, support vector machines (SVM), neural network and so on. SVM is a very potential promising classification technique which is proposed by AT & TBell laboratory research team leaded by Vapnik in1963. SVM with perfect mathematical form, geometric interpretation and generalization ability, solving problems about the model selection, less learning, over learning and nonlinear; avoiding the local optimal solution; overcoming the "curse of dimensionality" effectively, has been successfully applied to many classification problems with less artificial parameters [3]. This article uses radial basis kernel function to process ultrasonic echoes signals, and achieve automatic recognition by using one-against-rest classification and tree classification. The experimental results show that the method not only improves the recognition accuracy, but also improve the computing speed, and achieves good results.2 ULTRASONIC ECHO SIGNAL ANALYSISAs an integrative device, ultrasonic in-line inspection system contains complex mechanism, electronic instruments and a computer, as shown in figure 1.Ultrasonic inspection adopts the immersion pulse echo method, and a detector with probes arrayed in a circle to scan and inspect corrosion while walking through pipelines. Pipeline transportation medium is used as ultrasonic couplant material, such as petroleum. In the circle, different probes may be detected by the different pipeline wall, such as shown in figure 2, which is aProceedings of the 29th Chinese Control Conference July 29-31, 2010, Beijing, Chinacross-sectional diagram of ultrasonic probes arrayFig.1 An ultrasonic in-line inspection device of pipelinesPipeline wallinspection device Ultrasonic probepetroleumFig.2 Distribution of ultrasonic probesThe wall thickness at point A is greater than point B, while the probe points to the mutation interface at point C, and their ultrasonic echo signals are showed in figure 3.3(a) Ultrasonic echo signals at point A3(b)Ultrasonic echo signals at point B3(c)Ultrasonic echo signals at point CFig.3 ultrasonic echo signalsIt can be seen from figure 3(a) that the ultrasonic probe emits an ultrasonic pulse P towards the inner and outside walls at point A, firstly the probe receives reflection pulse F_a from the inner wall, secondly receives reflection pulse B_a from the outside wall. The wall thickness d2 is got through calculating the interval between the two echo signals F_a and B_a. Likewise, the distance d1 between the probe and the inner wall is got through calculating the interval between the transmitted pulse P and the inner interface echo signal F_a. According to corrosion depth and ultrasonic propagation velocity in the transmission medium and pipe, the positions of reflection pulse from the inner wall or from the outside wall will change at point B, as show figure 3(b), so d1 larger and d2 smaller, which means the wall thickness reduces. The information about the corrosion of pipeline wall and the transform of the pipeline can be judged. Although it is a direct calculation with simple principle, yet when the probe points to the mutation interface, because of the mutative ultrasound intensity received from two sides of the interface, the echo amplitude changes, that brings difficulties for the interval time with the two echoes. Likewise as show figure 3(c), the wall thickness d2 is got through calculating the interval between the first two echo signals F_c and F1_c, it will cause confusion. When the ultrasonic probe is moving or wobbling, there will be a certain confusion zone, which can not give a clear mutation interface, and result in a lot of noise in the C scan picture. Therefore clear and correct classification of mutations interface is the key of ultrasonic echo signals- processing.Because of the complicated condition in pipeline, ultrasonic detection echoes are affected by many factors such as the line defect orientation, the defect width, the branching-point geometry, wall roughness, working state and the interaction between different echoes [4]. The characteristic of time-domain is very complex, and there are thousands of scan data for an ultrasonic echo signal, so it is a high-dimensional classification and recognition problem. Ultrasonic echo signals are directly used as the feature vectors, with SVM advantage of solving the small sample, nonlinear, high-dimensional pattern recognition. To unify feature extraction and pattern classification is an effective multi-classifiers method for ultrasonic inspection.3 ULTRASONIC ECHO SIGNAL PROCESS WITHSVM MULTI-CLASSIFIERThe remaining pipe wall thickness represents the pipeline corrosion. The limited wall thickness is divided into several intervals and each interval corresponds to a kind of corrosion. So the ultrasonic inspection problem has become to process ultrasonic echo signals with multi-classifiers. The classifier is designed toautomatically classify a given data after study. The essence of classifier is a mathematical model. At present, different models have different branches, including: Bayesian Network Classifier, K-Nearest Neighbor (KNN), Vector Space Model (VSM), Neural Network, SVM and so on. The weak of Bayesian Network Classifier is that the overall probability distribution and probability distribution function (Density function) about various types of samples are unknown. In order to obtain them, the sample is large enough[5]. Large amount of calculation is the disadvantage of KNN, because each classified object must to calculate its distance from all the known samples in order to obtain its K-nearest neighbor. VSM requires calculation of classified space vectors, which largely depend on the category of vector contained in the feature items. Neural Network is based on the principle of risk minimization learning algorithm, also has some inherent flaws, such as difficulties to determine the number of layers and neurons, and easily fall into local minimum, even have the phenomenon of over learning [6]. While these deficiencies can be vanished in the SVM algorithm.The SVM algorithm is based on VC dimension theory and structural risk minimization principle. According to the limited sample of information in the model of the complexity, it seeks the best compromise between learning ability in order to obtain the best generic ability[7]. According to a preselected non-linear transformation, the input vector x is mapped to a high dimensional feature space Z; and then in this feature space, it will construct an optimal separating hyper plane, as shown in figure 4.The most basic theory of SVM is for the binary classifications, but in actual applications still need to solve the multi-classification problem. There are some multi-classification algorithms showed as follows. In the one-against-rest algorithm, n SVM classifiers, SVM i is trained for a n-class classification problem. During training, each SVM use the samples of its corresponding class as the positive examples and the samples from all other classes as the negative samples. During testing, the test samples are evaluated against all n SVM i and the SVM k which gives highest decision value as the predicted class is chosen[3], the structure is showed in figure 5（a）. In the one-against-one algorithm, one SVM classifier, SVM i,j , is constructed for every pair of classes (i, j). There are N(N +1)/2 SVM classifiers in total. During testing, the samples are evaluated against all the pairwise classifiers SVM i,j . The final decision of the class for the test samples are determined by a voting scheme. Every SVM classifier gives one vote for its predicted class. The class that receives most of the votes is chosen as the final class for the test sample [8]. Tree classification method is the improvement for the method of one again one. Firstly, k types of training samples divide into two classes; secondly, each class divides into two sub-classes; finally all the samples are separated [9,10], the structure is showed in figure 5（b）. In the Decision Directed A cyclic Graph algorithm, the same as the one-against one, N(N + 1)/2 pairwise classifiers are constructed. During testing, a list with all class candidates is created. For each test with the SVM, the class candidate which is given negative label by the SVM is removed from the list. The last class that is left on the list is chosen to be the class for the test sample [11]. Weston proposed twok-class SVM algorithms: QP-MC-SV algorithm and LP-MC-SV algorithm. Naturally, when construct the decision-making function, taking into account all of classes [11-12]. Platt et al proposed a new learning framework: Ball structural classification algorithm is using ultra balls to define the same type of data, and then the data space is changed into a super-ball formed by a lot of balls, like a collection of a lot of soap bubbles in the three-dimension[13].5(a) the one-against-rest algorithm5(b) the tree algorithmFigure 5The SVM multi- classification algorithmFor the multi-classifier, the known training samples divide into n sub-classes,n l n l n x x x x K K K ，，，，，11111 Where ki x belongs to the k th class.Consider the set of linear functionk k k b x x f +×=)()(w ,k =1，，K n (1)The aim is to construct n functions, making the rules ：m={}])[(,],)[(max arg 11n nb x b x +×+×w w K(2)can error-free separate training samples ,where the inequality[()][()]1k k m m i k i m x b x b w w ×+-×+≥ (3) for k =1，，K n,k m ¹ and i =1，，K k l are established.If the above inequality has a solution, å=nk k k 1),(w w hasminimum.If the training samples can not be error-free separated, Minimize functional is defined bykin k l i nk k kkC ååå===+×111)(x w w(4)With constraint conditions ：[()][()]1k k m m k i k i m i x b x b w w x ×+-×+-≥ (5)Where k =1，，K n,k m ¹and i =1，，K k l , C is penalty factor.To solute above inequality, Lagrange multiplier optimization technology is used to expand the function )(x f k in support vector, having the following expression:))(,()(1k ik m l i i k xx m k x f k×=åå¹=ak m j k m l j j b x x k m m+×-åå¹=))(,(1a (6)Where ),(m k i a is expansioncoefficient,k =1，，K n,k m ¹,i =1，，K k l ,j =1，，K m l . Minimize functional is described as follow:ååå=¹==nk k m l i i km k W 11),([)(a aåå¹*=*×-k m k j k i j l j i i x x m k m k k))(,(),((211,a a ))(,(),(11**×+*==ååm jm ij l i l j i x x k m k m mma a))])(,(),(211m j k i j l i l j i x x k m m k km×-*==ååa a(7)With constraint conditions:0(,)i m k m k C a ¹å≤≤ (8)),(),(11k m m k k m l j jk m l i imkåååå¹=¹==aa ,k =1，，K n (9) From the above derivation, it needs to construct SVM toreplace the corresponding formula )(s j r i x x × with kernelfunction )(s j r i x x K × [7].5 The EXPERIMENTAL RESULTS ofMULTI-CLASSIFIERSAdjustable high-speed ultrasonic capture card with the sampling frequency 100MHz and the ultrasonic probe with frequency 5MHz are used in this experiment. According to over-sampling techniques and the accuracy, the sampling frequency is 50MHz. The ultrasonic probe located above the plate does uniform motion, and detect planes or mutation interfaces between two planes. The material of the standard sample tube is close to the actual steel pipe, and the thickness of steel plate is ladder-like, respectively 25,19,12,6 mm. Different thicknesses planes simulate different normal wall, the mutation interface simulates the border of pipeline corrosion pit, layered corrosion, mechanical corrosion, as shown in figure 6.Figure 6 Pipeline Corrosion Inspection ExperimentA training classifier is often based on different technology or the same technology but with different parameters, or different training samples or different sample characteristics. The classifier by different methods has different decision spaces. According to the feature of the classifier, find out the optimal classifier which around the area of input samples, and consider output as classification results. Radial Basis Function)exp(),(22sji j i x x x x K --=, one-against-restalgorithm and tree algorithm are used in this experiment.Penalty factor C which is the normal number, determines the punishment degree beyond the limits of error of the sample. The different constant value C has different effects. When the value of C is bigger than a certain value, its change will decrease the impact of the analysis. In this experiment C= 15.The ultrasonic echo signals areput in the SVM model for automatic classification, results are showed as follows:7(a) Results of one-against-rest classifier7(b) Results of tree classifier Fig7 Four Classification ResultsThe correct rate is used to determine the SVM classification effect, showed as follows:Tab.1Correct rate of one-against-rest classifierTab.2Correct rate of tree classifierFrom Table 1 and Table 2 it can be seen that when the parameter s = 2, accuracy in various locations is relatively high. With the value s increasing, the correct rate is gradually lower. On this basis, the correct rate and classification time correspond to change, with ultrasonicecho signals in the proportions 1/2, 1/3, 1/4, 1/5, 1/6, as shown in table 3 and table 4.Tab.3 Correct rate of one-against-rest algorithmWithdifferent sample pointsTab.4 Correct rate of tree classifier Withdifferent sample pointsFrom Table 3 and Table 4 it can be seen that: With the reduction of sampling points, the time for classification also reduces, but the correct classification rate does not significantly reduce; Although the accuracy of tree algorithm is relatively higher than one-against-rest classification, compared with one-against-rest classification, the time for tree classification is relatively shorter and faster.6 CONCLUSIONIn practice, the problem of multi-class classification is needed to solve, so the study has an important practical value. Because of the complicated condition in pipeline, the recognition of ultrasonic detection echoes is difficult. Analyze ultrasonic detection echoes ’ data from experiments, use radial basis kernel function to process the signal, and achieve automatic recognition by using one-to-many and trees two ways. The experimental results show that the method not only improves the classified recognition accuracy rate, but also improve the computing speed, and achieves good results.REFERENCES[1] Tang Jian. Research on Ultrasonic Inspection of Pipeline Corrosion [D]. Beijing University of Chemical Technology, 2005.[2]DaiBo, Zhao Jin, Zhou Yan. Ultrasonic in-line inspection of pipeline corrosion based on support vector machine. Journal of Chemical Industry and Engineering (China), 2008, 59 (7): 1812-.1817.[3] Vapnik V N. The Nature of Statistical Learning Theory [M] 1New York: Springer Verlag, 2000.[4] Wu Dexin, Yang Xiaolin. Identification of waveforms anddefects in ultrasonic inspection. 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