Nonlinear, non-stationary image processing ...

Nonlinear, non-stationary image processing technique for eddy current NDE Guang Yang, Gerges Dib, Jaejoon Kim, Lu Zhang, Junjun Xin, and Lalita Udpa Citation: AIP Conference Proceedings 1430, 689 (2012); doi: 10.1063/1.4716293 View online: http://dx.doi.org/10.1063/1.4716293 View Table of Contents: http://scitation.aip.org/content/aip/proceeding/aipcp/1430?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Multiparametric 3D in vivo ultrasound vibroelastography imaging of prostate cancer: Preliminary results Med. Phys. 41, 073505 (2014); 10.1118/1.4884226 A novel computer-aided lung nodule detection system for CT images Med. Phys. 38, 5630 (2011); 10.1118/1.3633941 Feature selection using factor analysis for Alzheimer’s diagnosis using F 18 -FDG PET images Med. Phys. 37, 6084 (2010); 10.1118/1.3488894 EDDY CURRENT SIMULATIONS AND MEASUREMENTS OF SODIUM EFFECT FOR MAGNETIC AND NON MAGNETIC STEAM GENERATOR TUBES OF FBR AIP Conf. Proc. 1096, 580 (2009); 10.1063/1.3114308 Investigation of Frequency Mixing Techniques for Eddy Current Testing of Steam Generator Tubes in Nuclear Power Plants AIP Conf. Proc. 894, 603 (2007); 10.1063/1.2718026

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NONLINEAR, NON-STATIONARY IMAGE PROCESSING TECHNIQUE FOR EDDY CURRENT NDE Guang Yang1, Gerges Dib1, Jaejoon Kim2, Lu Zhang1, Junjun Xin1, and Lalita Udpa1 1

ECE, Michigan State University, East Lansing, MI 48823, USA Daegu University, Gyeongsan, Gyeongbuk 712-714, Korea

2

ABSTRACT. Automatic analysis of eddy current (EC) data has facilitated the analysis of large volumes of data generated in the inspection of steam generator tubes in nuclear power plants. The traditional procedure for analysis of EC data includes data calibration, pre-processing, region of interest (ROI) detection, feature extraction and classification. Accurate ROI detection has been enhanced by pre-processing, which involves reducing noise and other undesirable components as well as enhancing defect indications in the raw measurement. This paper presents the Hilbert-Huang Transform (HHT) for feature extraction and support vector machine (SVM) for classification. The performance is shown to significantly better than the existing rule based classification approach used in industry. Keywords: Eddy Current Inspection, Hilbert-Huang Transform (HHT), Support Vector Machine, Automatic Data Analysis, Feature Extraction, Rotating Probe Coil PACS: 41

INTRODUCTION Steam generators (SG) are the heat exchange unit used to generate steam and run the turbines in nuclear power plants [1]. These tubes are continuously exposed to high temperature, high pressure, high fluid flow rates and material interaction conditions which cause severe degradation. The tubes are typically made of Alloy 600 or Alloy 690 and metal deterioration may occur in the free span, tube sheet or tube support area. The flaws include mechanical wear between tube and tube supports, outer diameter stress corrosion cracking (ODSCC), pitting, volumetric degradation, primary water stress corrosion cracking (PWSCC), and inter granular attack (IGA). Eddy current (EC) NDE has been effectively applied in inspection of SG tubes. EC testing of SG tubes is usually utilized to identify and characterize flaws [2]. The bobbin probes and rotating probe coils (RPC) are commonly used EC probes for detecting SG tubes. Compared to bobbin EC data, the analysis of rotating-probe EC data is more complex. An automatic flaw detection system can provide accurate, consistent and rapid analysis of large volume of 2D data and discriminate between flaw and noise signals. The present approaches of automated signal classification systems applied neural network method or rule-based classification [3-4]. Neural network classification methods Review of Progress in Quantitative Nondestructive Evaluation AIP Conf. Proc. 1430, 689-696 (2012); doi: 10.1063/1.4716293 © 2012 American Institute of Physics 978-0-7354-1013-8/$30.00

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require derivation of the complex network structure and also the problem of over-fitting, which results in poor classification if the test data is not similar to the training data. The rule-based classification mimics a manual analyst, however, one needs to estimate the different threshold values for different data categories. This paper presents a novel automatic analysis of RPC data. The Hilbert-Huang Transform (HHT) is used to extract image features in the detected regions of interest (ROI). Novel features for measuring signal complexities are investigated. The SVM (Support vector machine) technique is then utilized to classify the data contained in the ROI. The ROI features based flaw classification has been validated sing experimental RPC data from 7 defect categories. ROTATING PROBE EDDY CURRENT INSPECTION In conventional EC testing of SG tube inspections, the bobbin coil probe composed of two nominally identical coils connected in a differential mode and excited at multiple frequencies has limitations in its ability to detect degradation in all regions of the tube (for example, expansion transitions). Furthermore, the bobbin probe has weakness in the ability to accurately size and characterize circumferential flaws. RPC probe has been extensively applied in the EC inspection of SG tubes. RPC generally has two types of probe coils namely, Pancake RPC and plus point RPC coils. Both RPC probes are sensitive to axial and circumferential flaws. Each of these coils is excited at multiple frequencies. The principal benefit of the RPC technique lies in the increased resolution over that of the bobbin coil probe. A typical RPC probe performs testing of a tube at high speeds; 900 revolutions per minute or higher, with recent probes capable of inspecting at speeds reaching 2500 rpm and moving forward in the axial direction at the rate of around 4” per second. By controlling the rotation rate, pull rate, and coil diameter, 100% coverage of the tube surface at high resolutions can be achieved. The detection and characterization of degradation has been improved by RPC probes [4-6]. The plus point coil consists of two coils that are oriented orthogonal to each other [6]. A typical configuration for PRC probes is shown in Fig. 1. AUTOMATIC EC DATA ANALYSIS Automatic signal analysis systems for bobbin coil EC data mainly focus on the relationship between flaw characteristics and the shape and orientation of the corresponding Lissajous pattern of the EC signal, which have been well studied [7]. The analysis of RPC probe is typically carried out by first displaying the data in form of a C-

SG tube Axial direction

Eddy current coils

FIGURE 1. Typical PRC probe configuration.


scan image. Image based automated analysis algorithms are then used for identifying regions in the tube with potential flaws [8-9]. This paper presents an automated flaw detection system for analyzing 2D RPC data using signal features and SVM classification to identify various defect types. Fig. 2 shows a schematic of the overall approach of the automated data analysis system. The RPC raw data is read and calibrated using standard procedures. The preprocessing module is applied to reduce noise and enhance flaw signal for accurate ROI detection. The new feature extraction and classification modules are described in detail in the next section. ROI Detection In automatic signal analysis, a sequence of processing procedures are used to process the raw data from the entire tube length, identify data segments defined as region of interest (ROI), which require further analysis, estimate discriminatory features and classify the image data in the ROI as a flaw or non-flaw signal. A high-pass filter based algorithm is applied as the preprocessing step to enhance the RPC image data. The SG tubes are inspected using multi-frequency plus point coil and pancake coil probes. An adaptive threshold algorithm is proposed to implement the identification of ROIs. The algorithm uses prior information to separate: (i) above tube sheet, (ii) support plate, (iii) tube sheet and (iv) tube sheet transition regions into independent areas. Since noise is in general, time-varying, the histogram of each area is calculated and the different threshold parameters are estimated in each area. The local thresholds in each area to detect high amplitude signals and detects relevant parts of the overall data. The calibrated EC image data with noise is shown in Fig. 3. The ROI detection using adaptive thresholding technique is presented in Fig. 4. Feature extraction and classification steps are performed only on the data within the ROI.

FIGURE 2. Schematic of the procedure of EC data automatic analysis.


Tube sheet region

Tube sheet transition region

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FIGURE 3. Sectioning of ROI area in 2D PRC data.

ROI

FIGURE 4. ROI detection result of 2D PRC data.


DATA CLASSIFICATION Classification is the process of assigning signals into one of several known classes. Features are first calculated from the data within the 2D ROI. Typically, features are physical features computed using the EC signal characteristics. The second type of features is transform-based techniques. In this paper, novel nonlinear and non-stationary HHT transform based features are extracted to capture signal complexity and characteristics. Classification is accomplished using SVM technique. HHT Transform and Empirical Mode Decomposition The empirical mode decomposition (EMD) method is a nonlinear and nonstationary signal processing technique, which uses an iterative algorithm to decompose a signal into a set of oscillatory modes called intrinsic mode functions (IMF) [10]. The Hilbert transform of each intrinsic mode provides the amplitudes and instantaneous frequency as functions of time and is referred to as the Hilbert amplitude spectrum. An IMF is a function that represents oscillation characteristics embedded in the data. The EMD method is defined by two conditions: (i) Number of extrema and number of zero crossings must equal or differ at most by 1. (ii) At any point, the mean value of envelope defined by local maxima and envelope defined by local minima is zero. The IM functions of a signal are extracted using a systematic sifting process. In each iteration, the mean signal is calculated from the envelopes and subtracted from the original signal until the two conditions are satisfied. The overall procedure for deriving the IMF of given signal x is as follows: Step 1: Initialization: set ε the stopping criterion and iteration j=1 Step 2: r j −1 = x (defined as residual) Step 3: Extract the j th IMF (a) i = 1 (defined as number of sifts), h j ,i −1 = rj −1 (b) Identify local maxima/minima of h j ,i −1 (c) Construct upper and lower envelopes U j ,i −1 and L j ,i −1 by cubic spline interpolation with local minima/maxima of h j ,i −1 (d) Compute the envelopes mean: (1) m j ,i −1 = (U j ,i −1 + L j ,i −1 ) / 2 (e) Update: h j ,i = h j ,i −1 − m j ,i −1 (f) Calculate stopping criterion: | h j ,i −1 − h j ,i |2 SD(i ) = ∑ h 2 j ,i −1 (g) Repeat step (b)-(f) until SD(i ) < ε and then put IMF j = h j ,i Step 4: Store IMF j = h j ,i as the IMFs. Step 5: Update residual: r j = r j −1 − IMF j

(2) (3)

(4)

Step 6: Repeat step 3 with j = j + 1 until the number of extrema in rj < 2 On convergence of the iterative procedure hj contains the jth IMF. In order to get the IMFs, the procedure is repeated after the current IMF is subtracted from the signal as seen in Step 5.


Feature Extraction The 2D ROI box is transformed to a 1D signal by concatenation of rows and then processed by the EMD procedure. The IMFs represent simple oscillatory characteristics embedded in signal. Therefore, the signal complexity embedded in the IMFs serves as classification features. The IMFs of the 1D signal from an ROI are shown in Fig. 5 and the first two levels of IMFs that present the most information of the signal are retained for feature extraction. The parameters measuring signal complexities, namely, Root mean square (RMS), Variance and Shannon entropy [11] and statistical parameters – Skewness and Kurtosis are computed for each IMF. For IMF1 and IMF2, corresponding to an ROI, we therefore have a 10 dimensional feature vector. SVM Classification The SVM method was introduced by Boser, Guyon and Vapnik [12]. Neural networks classification techniques used in previous papers [4-5] are difficult to train without large amount of training data [13]. On the other hand, as a linear classifier, SVM method provides a compromise between the parametric and nonparametric approaches. SVM method estimates a linear decision function by mapping the data into higherdimensional feature space. This mapping is characterized by the choice of a class of functions defined as kernels, which are computed for SVM optimized solution [12]. The advantages of SVM technique are that it is less prone to over-fitting, offers a kernel based classification, less complexity and unique solution. Extracted features of calculated IMFs are applied as inputs of SVM method. Additional classes may also be easily added as the algorithm is modular. Table 1 presents typical detection results of RPC data from seven different databases at different locations and degradation types. The performance of classification over 391 ROI signals is seen to be around 98% with one false call and one missed call. These results indicate the feasibility of the proposed automated classification algorithms for the analysis of eddy current steam generator data.

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FIGURE 5. IMFs of a ROI box signal by the EMD decomposition.


TABLE 1. Classification result of PRC EC data set. EC data set Category freespan_odiga_pluspt freespan_idiga_pluspt tscrevice_odiga_pluspt UbendSupport_wear_pluspt abovets_odiga_pluspt eggcrate_wear_pluspt Tsexpanded_pwscc_circ_pluspt

Total flaws 37 19 44 38 20 7 19

Detected flaws 36 19 44 38 20 7 19

False calls 1 0 0 0 0 0 0

Miss calls 1 0 0 0 0 0 0

CONCLUSIONS A fully automated signal analysis algorithm for detecting flaws in SG tubes is presented. The EC inspection data is pre-processed to detect regions of potential flaw indications. Features based on Hilbert Huang Transform in conjunction with Support Vector Machine for classification have been used for the analysis. Initial results indicate that the method is very promising. The classification performance obtained using the proposed approach is a significant improvement over current methods that implement feature extraction and classification applied to raw ROI data. More extensive testing of the overall algorithm is in progress. ACKNOWLEDGEMENTS This work is supported by the EPRI Foundation. We thank J. Benson and R. Williams for technical assistance. REFERENCES 1. S. Chuang, Eddy current automatic flaw detection system for heat exchange tubes in steam generators, PhD Thesis, Iowa State University, 1997 2. L. Udpa and W. Lord, “New approaches for multifrequency ECT of steam generator tubes,” Proc. of the ISMM Int. Symposium, Honolulu (USA), ACTA Press, pp.108-112, February, 1988. 3. P. Xiang, S. Ramakrishnan, X. Cai, P. Ramuhalli, R. Polikar, S. S. Udpa and L. Udpa, “Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes,” Intl. J. Applied Electromagnetics and Mechanics (Invited paper), Vol. 12, No. 3/4, pp. 151-164, 2001. 4. S. Majumdar, S. Ramakrishnan, P. Ramuhalli, L. Udpa, S. Udpa, J. Benson, R. Williams and T. U. Bipes, “Automated data analysis system for steam generator tube inspection”, Mater. Eval., Vol. 69, No, 2, pp. 201-207, 2011. 5. L. Udpa, P. Ramuhalli, J. Benson, and S. Udpa, “Automated analysis of eddy current signals in steam generator tube inspection”, 16th World Conference on Nondestructive Evaluation, Montreal, Canada, 2004. 6. R. Gracin, D. Barilar, “Detecting of cracks by using +point eddy current probe”, International conference nuclear option in countries, Opatija, Croatia, 1996. 7. S. S. Udpa, L. Udpa, “Eddy Current Nondestructive Evaluation”, Wiley Encyclopedia of Electrical and Electronics Engineering, edited by John G. Webster, Vol. 6, pp. 149-163, 1999


8. B. Upadhyaya, M. Behravesh, W Yan and G. Henry, “An automated diagnosis system for eddy current analysis using artificial intelligence techniques”, Proc. 14th WCNDT, New Delhi, India, December 8-13, 1996. 9. M. Hayakawa, V. Cingoski, K. Kaneda and H. Yamashita, “Evaluation of the Characteristics of a Rotating Eddy-Current Probe for ECT Using Edge FEM”, Proc. ENDE ’97, Reggio Calabria, Italy, September 14-16, 1997, pp. 170-179. 10. N. E. Huang, S. Zheng, et al, “The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis”, Proc. of the Royal Society of London Series A―Mathematical Physical and Engineering Sciences, Vol 454, pp.903-995, 1998. 11. B. E. BOSER, I. GUYON, V. VAPNIK, “A training algorithm for optimal margin classifiers”. Proc. Fifth ACM Workshop on Computational Learning Theory (COLT), pp. 144.152, 1992. 12. J. M. Moguerza, A. Muñoz, “Support Vector Machines with Applications”, Statis. Scien., Vol. 21, No. 3, pp. 322.336, 2006.
