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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
Window Feature-Based Two-Stage Defect Identification Using Magnetic Flux Leakage Measurements Jinhai Liu , Member, IEEE, Mingrui Fu, Student Member, IEEE, Feilong Liu, Senior Member, IEEE, Jian Feng, Member, IEEE, and Kuangqing Cui (Invited Paper) Abstract— Magnetic flux leakage (MFL) testing, one of the nondestructive testing methods, is widely adapted by approximately 90% of in-service pipelines. It is very important to identify defects in MFL testing. This paper presents a method to detect defects and determine precise defect regions using window features generated from MFL measurements. The main contributions are: 1) four novel window features of defects, namely, saliency, contrast, center point (CP), and fingerprint with parameters, are presented, which provide sufficient information for defect identification. In particular, a Bayesian method is developed to estimate the near-optimal parameters of these features and 2) a novel two-stage identification process is proposed, which can not only detect defects, but also segment the complicated defect regions precisely. The performance of the proposed method is demonstrated by real MFL measurements collected from experimental pipelines and in-service pipelines, respectively. The results show that the proposed method has satisfied accuracy for defect identification of experimental and engineering application. Index Terms— Boundary determination, defect identification, defect segmentation, magnetic flux leakage (MFL).
I. I NTRODUCTION
M
AGNETIC steel pipelines are widely used for petroleum and gas transmission, and they should be seriously considered, because the risk of potential leakage rises as in-service time increases. As one of the key precautions to prevent leakage, nondestructive testing (NDT) [1], [2] is widely utilized. Currently, NDT methods used in pipeline inspection include magnetic flux leakage (MFL) testing [3], [4], eddy current testing [5], [6], and ultrasonic testing [7], [8]. Among them, MFL testing is widely adapted by approximately 90% of in-service pipelines. The advantage of MFL testing is beyond Manuscript received February 21, 2017; revised August 5, 2017; accepted August 8, 2017. Date of publication October 11, 2017; date of current version December 7, 2017. This work was supported in part by the National Key R&D Program of China under Grant 2017YFF0108800 and in part by the National Natural Science Foundation of China under Grant 61473069, Grant 61374124, Grant 61627809, and Grant 61673093. The Associate Editor coordinating the review process was Dr. Amitava Chatterjee. (Corresponding author: Jinhai Liu.) J. Liu, M. Fu, F. Liu, and J. Feng are with the College of Information Science and Engineering, Northeastern University, Shenyang 110004, China (e-mail:
[email protected];
[email protected];
[email protected];
[email protected]). K. Cui is with CNOOC EnerTech Equipment Technology Co., Ltd., Tianjin 100024, China (e-mail:
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIM.2017.2755918
dispute due to: 1) low limitation for testing environment that can detect both internal and external defects without being affected by the transportation medium [9]–[11] and (2) the capability of initial quantification for defects. MFL measurements are collected from MFL testing. There are mainly two applications for MFL measurements. One refers to defect identification, and the other refers to defect inversion. Defect identification includes defect detection and defect region determination. In this paper, defect identification in MFL mainly relies on measurements or images. In [12], multiple eigenvalues of MFL measurements were applied to identify defects and to inverse defects. However, it merely uses defect peak values. In [13], global image reconstruction scheme using the singular value decomposition instead of local features of textures was proposed to detect defects. In [14], a method to cluster and analyze corrosion defects by the hidden Markov random field model and the Bayesian model was proposed. In [15], a detection method was introduced to process MFL measurements and identify defects based on multisensor. In [16], the use of support vector machine (SVM), kernelized principal component analysis, and kernelized partial least squares were discussed to process the MFL images. In [17], traditional features, including area, average amplitude, and peak-to-peak, were introduced. However, these features merely consider geometric and statistical attributes and hence lack of the ability of region determination. In [18], several different wavelet feature sets in different decomposition levels were applied to identify defects. All the above-mentioned studies were effective for defect identification in simulations or in certain conditions. However, they have poor performances in practical applications, especially in complicated defect conditions. Detect inversion, computing the size of defects, is one of the main goals of MFL testing. This problem has been researched on by some researchers. In [19] and [20], relationships between MFL measurements in defect regions and size of defects were established using model-free methods to estimate the size computation of defects. In [21] and [22], physical models were applied to achieve size computation of defects. In [23], velocity effects on detect inversion were discussed. Most of these detect inversion methods have considered the defect regions as input. Thus, the accuracy of defect
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LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
Fig. 1.
Schematic of MFL inner testing.
inversion seriously depends on the precision of defect regions. Obviously, precise defect regions are the premise of detect inversion. In practice, there are often many defects close to each other, which is challenging for precise defect region. Overlarge or oversmall determination of defect regions will lead to detect inversion failure in any of these methods. Unfortunately, few papers focus on precise defect region determination. In summary, both defect detection and precise defect region determination in defect identification are required for a better system. Considering that this paper proposes a novel defect identification method based on window features using MFL measurements to detect defects and determine defect regions. It has the following merits. 1) Four novel features, namely, saliency, contrast, CP, and fingerprint, have been introduced in this paper. These features include information inside and outside detected regions, which is helpful for defect detection and precise defect region determination. To guarantee correct feature extraction, a Bayesian method is developed to estimate the approximate optimal parameters of the features. 2) Different from the simple identification process, a complete process of two-stage defect identification is established, which promotes the automation level on the one hand, and on the other hand, the segmentations of complex defect regions are discussed to further extract accurate defect regions, which provides input guidance for defect inversion. The rest of this paper is structured as follows. Section II describes the problem. Section III introduces the method of defect identification and the corresponding subprocesses. The experiments are illustrated in Section IV. Section V concludes this paper. II. P ROBLEM F ORMULATION A. Principle The classic magnetic circuit of MFL testing used in pipelines consists of iron, magnets, steel couplers, and pipe wall, as shown in Fig. 1. The wall loss in pipelines will lead to magnetic leakage flux increase. And the magnetic sensors placed between two magnets can collect changes of magnetic leakage flux. By analyzing the changes, defects and other anomalies can be detected. B. Problem Statement The aim of this paper is to realize defect identification, which includes defect detection and defect region determination. Defect detection tries the best to find defects
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from MFL measurements without false and miss inspection. Defect region determination is to avoid overlarge or oversmall regions. There are two challenges in defect identification problem: 1) MFL measurements contain many noises and other components, which lead to difficulties of defect detection and 2) some defects are close to each other. Precise defect regions of these defects are difficult to be determined, which has effects on defect inversion accuracy. To deal with that, defects should be detected accurately, and defects close to each other need to be segmented into single defects precisely. In order to better state identification problem, some definitions are presented as follows. 1) Anomaly: Anomaly is an indication, detected by nondestructive examination of an irregularity or deviation from the base pipe or sound weld materials, which may or may not be a defect [24]. 2) Axial Profile Measurements: For a given region with size of N × M, axial profile measurements (APMs) α = (X 1 , X 2 , . . . X i ) are computed by Xj =
N 1 xi j . N
(1)
i=1
3) Circumferential Profile Measurements: For a given region with size of N × M, circumferential profile measurements (CPMs) β = (Y1 , Y2 , . . . Yi ) are computed as Yi =
M 1 xi j . M
(2)
j =1
4) Approximate Center Point: Approximate CP (ACP) is a point to identify the center of defect measurements. A given region can be divided into grids, if the following conditions are satisfied, there will be an ACP in one grid: A Ai, j
Case1:RG < i < C G ; RG < j < C G = MAX(A Ai−1, j , A Ai+1, j , A Ai, j , A Ai, j −1 , A Ai, j +1 ) Case2:M AE(A Ai, j ) ≥ θs
where
1
1 (X i − X )2 . X n
MSE(X) =
(3)
i=1
A Ai, j indicates the average value of grid G i, j . G with a size of C G × RG is a matrix with all grids. θs is a parameter to be estimated. An AC P identification of one defect region is shown in Fig. 2. The unit of “Channel” is number. Each channel indicates a sensor. The curves are acquired from the acquisition channels, and the curves and the channels are one-to-one correspondence. 5) Unimodal Defect Measurements and Bimodal Defect Measurements: With reference to Fig. 3, unimodal defect measurements (UDMs) and bimodal defect measurements (BDMs) are defined by BDM f p(w) = 2, Case1 ST(w) = (4) UDM f p(w) = 2, Case2. 1 • is an operator and indicates the number of elements.
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Fig. 2.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
Diagram of AC P identification.
Fig. 5. Diagram of defect regions. (a) 2-D diagram of MDR. (b) 2-D diagram of IDRs. (c) Corresponding 3-D diagram of MDR. (d) Corresponding 3-D diagram of IDRs.
Fig. 6. Fig. 3. Definitions of bimodal defect and unimodal defect. (a) MFL measurements of bimodal defect. (b) APM of bimodal defect. (c) MFL measurements of unimodal defect. (d) APM of unimodal defect.
Fig. 4. Distance of two defects (t1 indicates axial distance and t2 indicates circumferential distance.) (a) Axial distance of two defects. (b) Circumferential distance of two defects.
Case1: |L 1 − L 2 | ≤ 0.1 × max(L 1 , L 2 ) ≤ L 3 Case2: |L 1 − L 2 | ≤ 0.1 × max(L 1 , L 2 ). 6) Independent Defect Regions and Multidefect Regions: With reference to Fig. 4, independent defect regions (IDRs) and multidefect regions (MDRs) are defined by IDR if max(t1 , t2 ) ≥ 6t (5) DR(w) = MDR otherwise where t indicates the wall thickness of the pipes. Two defect regions are shown in Fig. 5. In Fig. 5(a), green rectangle represents MDR. In Fig. 5(b), rectangles indicate IDRs, including UDM (black) and BDM (red).
Desired behaviors of defect identification process.
Fig. 5(c) and (d) is the corresponding 3-D diagrams of MDR and IDRs. “MFL response” represents the measured leakage magnetic field intensity (its unit is Gauss). Based on these definitions, a complete defect identification process is shown in Fig. 6. MFL measurements are divided into anomaly regions and nonanomaly regions. Anomaly regions include defect regions and nondefect regions. Among defect regions, if the region is an IDR, it should be detected and determined the precise region. If it is an MDR, segmentation should be executed to divide it into single defect regions (SDRs), including UDMs and BDMs. After defect identification, all output regions are identified as SDRs. In this paper, the problems to be solved are as follows. 1) Detect SDRs accurately from MFL measurements. 2) Ensure precise defect regions of SDRs. III. M ETHOD OF D EFECT I DENTIFICATION Fig. 7 shows the method of defect identification using MFL measurements. It includes two parts. Part 1 (Detection Model Development): MFL measurements are collected to generate defect and nondefect samples. After that, the novel features are extracted, and a Bayesian method is applied to estimate the near-optimal parameters of these features. Finally, a detection model is established by machine learning methods. Part 2 (Defect Identification): Defect regions are achieved in this part for the inspected MFL measurements. First, anomalies
LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
Fig. 7.
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Framework of defect identification. Fig. 9. Success and failure of saliency. (a) Success: windows containing SDRs have high saliency. (b) Failure: windows containing nondefects also have high saliency.
Fig. 8.
Image of ILI tool.
are inspected from MFL measurements. Second, a process of two-stage defect identification is developed to detect defects and determine precise defect regions. A. Detection Model Development 1) Measurement Acquisition: Inspection device, referred to as pipeline inspection gauges, is a self-contained device. This device is designed for autonomous operation in the pipeline and to collect MFL measurements from the inspected pipes. Fig. 8 shows a typical picture of in-line inspection (ILI) tool. The functionality of the ILI tool in this paper consists of five modules: record section, magnetic section, battery section, attitude section, and mileage section. These five sections are interconnected by articulating joints to adapt to the operation of pipe bend, rotation, and other conditions. Suppose totally n sensor nodes are deployed. The inspecting period includes m time slots. Each sensor node responds to its sensor measurement once per time slot. x i, j denotes the sensory measurement of point i at node j , where i = 1, 2 . . . m and j = 1, 2 . . . n. In this paper, there are 108 sensors. The sampling frequency is 1 time/2 mm. 2) Sample Collection: Sample sets, including defects and nondefects, greatly determine modeling accuracy. Defect samples are manually marked to get the whole detect information and precise regions, while nondefect samples include samples in defect regions and samples in nondefect regions. a) Nondefects samples in defect regions: Let the size of a defect be m ∗ n, the center coordinate is X (x, y)2 n m ,n − . (6) X = m− 2 2 Then, nondefect samples can be generated according to X, which should satisfy the following conditions. In order to differentiate defect and nondefect samples, and to ensure nonobjects containing partial information of defects, the center coordinates X (x , y ) of nondefects should satisfy. Case 1 : |x − x | ≥ m2 ,|y − y | ≥ n2 . Case 2 : The sizes of nondefects equal to the sizes of the defects. 2 • is an operator and indicates round toward negative infinity.
Here X (x , y ) is the center coordinates of nondefects, which is selected randomly. b) Nondefects samples in nondefect regions: In order to generate simple and efficient nondefect samples, nondefect samples are obtained in nondefect regions, which are limited as w ∩ = 0
(7)
where indicates objected defect regions. 3) Window-Based Feature Extraction: In this section, we present four novel features, namely, saliency, contrast, CP, and fingerprint feature to help defect detection and precise defect region determination. a) Saliency: In reality, a real defect has a different appearance from its surroundings. Based on this uniqueness, we refer the literature [25] to define the saliency feature for identified region w as x i −x f | xmax | −x (8) I(x i ) × S(w, θs ) = I x i ∈w (x i ) x i ∈w 1 if ≥ θs f () = (9) 0 if < θs where w indicates identified region, which changes with the sizes of defects in real detection environment. The smallest size of w is s1 × s2 , which is estimated in the following paper. x i is the element of w, and |x i2 − x 2w | is the energy of x i . Due to 0 ≤| (x i − x/x max − x) |≤ 1, θs ranging from 0 to 1 is a parameter to be estimated, which indicates the degree of local point value deviating from the mean value. This feature is designed to measure whether w contains an anomaly or not. As shown in Fig. 9, cyan rectangular window region indicates w. θs is the parameter to find salient points in w. S(w, θs ) is the measure to compute the overall saliency. In Fig. 9(a), values of w containing SDRs have high saliency. However, saliency may lead to identification failure. In Fig. 9(b), w containing nondefects also have high saliency, while these anomalies belong to the additional metal rather than defects. b) Contrast: Two contrast features, outer-ring contrast (OC) feature and inner-ring contrast (IC) feature, are developed to consider the relationships between defects and their related surrounding regions. These features reflect the size of backgrounds (nondefect regions) inside and outside w.
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
where θIC ∈ {θICL ,θICC }. θIC is the parameter to be estimated. IC(w, θIC ) is the region between window w and the corresponding inner ring. It is obvious that θICL and θICC range from 0 to 1. I C is computed by IC(w, θIC ) =
Fig. 10. Diagram of ring (black solid box indicates w, while yellow box means outer rings and red box means inner ring. we only plot two inner rings and two outer rings to explain.) (a) MFL measurements. (b) MFL image.
As shown in Fig. 10, an MFL image is used to better illustrate the ring. Some corresponding definitions are as follows. Ring: It is rectangular border obtained by shrinking and expanding w along all directions simultaneously. Axial Ring: It is rectangular border shrinking and expanding only along the axial direction of w. Circumferential Ring: It is rectangular border shrinking and expanding only along the circumferential direction of w. In actual identification, each contrast feature can be decomposed into axial-ring contrast feature and circumferential-ring contrast feature. Correspondingly, we define that i) Outer-ring contrast: OC is a measure of difference between w and its outer surrounding region. OC(w, θ OC ) is the outer region between w and corresponding outer-ring, which is obtained by enlarging w by the factor θOC OC(w, θOC ) = θOC w
(11) (12)
OC shows if the window w includes the whole defect region. The higher OC is, the greater possibility window w includes the whole defect in the same θOC . As shown in Fig. 11(a), w with whole SDR shows high OC. In view of the different shapes of defects, OC of window w, including MDR, is also high in Fig. 11(c). In addition, if the defects are close, there will be new defects in outer ring after expansion, which leads to the low OC in Fig. 11(b). ii)Inner-ring contrast: I C is a measure of difference between window w and its inner region. I n(w, θIC ) is the inner region inside inner ring, which is obtained by shrinking window w by the factor θIC IC(w, θIC ) = θIC w
(13)
(14)
I C can show the scale of inner background in w. The low I C represents large inner background. On the contrary, the high I C represents small inner background. In the same θIC , the successful identification is shown in Fig. 12(a), where window w includes SDR with small background. Besides, I C is low in Fig. 12(b), where w includes the whole SDR with more backgrounds. Unfortunately, I C is high in Fig. 12(c), where w includes MDR, which is not in accordance with desired behaviors. c) Center point: The C P feature is proposed to measure the dissimilarity between the center measurements and edge measurements. C P can also be obtained by shrinking the inner ring. With reference to (13) and (14), C P can be defined as C P(w, θCP ) =
E IC(w,θCP ) − E I
C(w,θCP ) 2 xi
(15)
x i ∈w
where E IC (w, θCP ) =
(10)
where θOC ∈ {θOCL ,θOCC }. θOCL and θOCC are two component parameters to control the outer axial ring and circumferential ring, respectively. It is noticed that the outer ring is not infinitely expanded, and the size of OC(w, θOC ) is 2 time as much as that of w. Thus, θOCL and θOCC range from 0 to 2. The OC between w and its outer surrounding region is computed as the chi-square distance OC(w, θOC ) = χ 2 (MSE(w), MSE(OC(w, θOC ))) (y − x)2 . χ 2 (x, y) = x
M S E(IC(w, θIC )) . M S E(I C(w, θIC ))
E IC(w,θ = CP )
x j ∈IC(w,θCP )
x 2j
IC(w, θCP ) x k2
(16)
IC(w, θCP )
(17)
x k ∈IC(w,θ CP )
IC(w, θCP ) = θCP . IC(w, θCP )
(18)
θCP ∈ {θCPL , θCPC }. θCP is the parameter to be estimated. d) Fingerprint: As shown in Fig. 13, the profile measurements of Fig. 13(a) and (b) are obtained from Fig. 3(a) and (c), respectively. It can be seen that there are probably one or two peaks of APM, while there is only one peak of CPM. Based on this character, fingerprint feature defined as F L(w) and FC(w) is presented to measure the number of peaks in APM and CPM FL (w) = f p(dt (x) ∗ α) FC (w) = f p(dt (x) ∗ β)
(19) (20)
where dt is a low-pass filter to obtain low-frequency measurement information. f p(•) is a function to get the number of peaks. 4) Parameter Estimation: The parameters of four proposed window features are estimated using the real MFL measurements collected from experimental pipelines. The precise defect regions are marked manually. The parameters to be estimated are θs , θOC , θIC , and θCP . The last three are estimated in the same method, while θs is estimated by a specialized method.
LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
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Fig. 11. Success and failure of OC. (a) Success: cyan windows containing SDRs have high OC. (b) Failure: cyan window containing SDR has low OC. (c) Cyan window containing MDR has also high OC.
Fig. 12. Success and failure of inner ring contrast. (a) Success: cyan windows containing SDRs have high IC. (b) Failure: cyan window containing SDR has low IC because of more backgrounds. (c) Cyan window containing MDR has also high IC.
defects at each scale s, which can be found by maximizing max BA (22) s ∗ = arg max ∗ s
∈ obj
where BA = Fig. 13. Comparison of APM and CPM with BDM and UDM. (a) APM and CPM of BDM. (b) APM and CPM of UDM.
a) Estimating the parameters of θOC , θIC , and θCP : θOC , θIC , and θCP are estimated in a Bayesian framework. Because all three parameters are estimated in the same manner, we restrict the explanation to θ = θOC and G(w, θ ) = OC(w, θ ). we define that defects are obj and nondefects are bg . For any value of θ , the likelihoods for the positive p(G(w, θ )|obj) and negative classes p(G(w, θ )|bg) can be built. Thus, the optimal θ ∗ can be found by maximizing the posterior probability that windows with defects are detected as positives θ ∗ = argmax
pθ (obj|G(w, θ )
w∈ obj
= argmax
pθ (G(w, θ )|obj) · pθ (obj) pθ (G(w, θ )|c) · pθ (c) obj
(21)
w∈
where the priors are set by relative frequency: pθ (obj) = obj /( obj + bg ) and pθ (bg) = 1 − pθ (obj). b) Estimating the parameters of θs : The optimal θs is estimated by optimizing the boundary accuracy (BA) of
w ∩ w ∪
(23)
that is, we seek the θs∗ that leads w to most accurately cover the object defect . In particular, BA is to evaluate defect regions, which is also applied in experiments. 5) Model Development: After anomaly inspection, the inspected anomalies need to be divided into defects or nondefects by some ways. Machine learning methods are used to complete the task in this paper. The detection process needs to select and prepare the f eatur es. The set of features χ defines points in a space. Features with the same label are selected to be classified into the same class. Thus, the classifier determines the optimal boundaries among different classes. The detection process can be regarded as a mapping problem. A point χi from the f eatur es set space can be mapped into a discrete space corresponding to the respective classes. This mapping problem is described as yi = f (χi , ), i = 1, . . . , h¯
(24)
where the variable yi corresponds to the class of the i th sample data and indicates the parameters of the function f . f maps the point in feature space into the discrete space. There are many methods to determine f . In this paper, three classifiers, including SV M, random forest (R F), and k-nearest neighbor (K N N), are applied to address this detection problem.
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
Fig. 14.
Schematic of anomaly inspection.
Fig. 15.
Flowchart of two-stage defect identification.
B. Defect Region Determination 1) Anomaly Inspection: The schematic of anomaly inspection is shown in Fig. 14. MFL measurements are divided into grids by the size of s1 × s2 . s2 is set to 1 channel in this paper, which means every grid measurements come from only one channel to reduce effects among neighboring channels. s1 is estimated in Section IV. Next, the anomaly grids are found by the threshold θs in the saliency feature. If the following condition is satisfied, the grids can be regarded as an anomaly: n 1 x i − x (25) ≥ θs . x n max − x i=1
In the end, the anomaly grids are interacted as anomaly regions. 2) Two-Stage Defect Identification: Fig. 15 shows the flowchart of two-stage defect identification. This process is introduced to identify defects with two main stage: 1) IDR identification (IDRI) stage (Step 1 ∼ 2). It provides the identification for those I D Rs, which consists of identification of UDMs and BDMs and 2) MDR segmentation stage (Step 3 ∼ 5). This stage is designed to identify the MDRs and to segment each MDR into SDRs. The architecture flow can be described as follows. Step 1 (Anomaly Collection): This step is to prepare identified anomalies for IDRI. These anomalies contain two parts. One is the original anomalies generated by anomaly inspection to be prepared for UDMs identification. The other is the new anomalies by anomaly interaction, which tries to interact close anomalies to further identify the BDMs. If two anomalies
are considered interacting, t1 is less than 6t with reference to Fig. 4. Step 2 (Model Detection): The machine learning method is applied to detect defects. If the results are positive, output them by overlap testing process, otherwise continue to next step. Step 3 (ACP Inspection): This process tries to seek out defect locations of anomaly regions to provide guidance of sampling windows. If there is no ACP, the local region is detected as nondefect, otherwise go on next step. Step 4 (Sampling Windows): Each ACP is related to a defect. This step tries to sample windows surrounding each ACP to prepare the inspection regions. The algorithm of sampling windows is described in Algorithm 1. In addition, it is noticed that we should update the AC Ps to detect the BDMs. If the following two cases are satisfied, there is a new AC P between AC Pi and AC P j . Case1 : C(AC Pi , AC P j ) ≤ ρ). Case2 : A( AC Pi AC P j ) = min(A( AC Pi AC Pk )). where C(x, y) indicates the circumferential distance of x and y and A(x, y) indicates the axial distance. In this paper, ρ is set to 3 channel. Then, the sampling process of new AC Ps is the same as Algorithm 1. Through the feature extraction and the model detection of sampling windows, MDRs can be divided into SDRs. Algorithm 1 Sampling Widows 1: N A ← the number of AC P of local MDR. 2: for i = 1 → N A do 3: Initialize the minimum window ws1 ×s2 . 4: repeat 5: Update new window w toward axial and circumferential direction simultaneously. 6: if ∃AC P j ∈ W, j = i then 7: Determine the extended boundary and sampling process continues. 8: else 9: Save window w. 10: end if 11: until Expansion process stops. 12: end for Step 5 (Model Detection):If the results are positive, output them by overlap testing process, otherwise continue to next step. Step 6 (Overlap Testing): When the output is positive, this process is executed to eliminate overlap and to achieve accuracy defect regions. If the following condition is satisfied, the two output regions can be combined: D1 ∩ D2 ≥ζ D1 ∪ D2
(26)
where D1 and D2 indicate any two output regions. We follow the widespread PASCAL criterion [26], and ζ is set to 0.5.
LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
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TABLE I C OMPARISON R ESULTS OF B A W ITH D IFFERENT C LASSIFIERS
outcomes, the Recall must be 100% while the Pr eci si on is low. Thus, we compute another evaluation criterion F to get the weight result of Pr eci si osn and Recall Fig. 16.
Learning parameter of θs .
IV. E XPERIMENTS The proposed method is evaluated by MFL measurements from experimental and in-service pipelines. MFL measurements from experimental pipelines are used to establish the method. The length of experimental pipelines is 800 m, along with 219 mm of outer diameter and 9.5 mm of wall thickness. The pipe material is carbon steel, which is used in North America for oil and natural gas pipelines. The velocity of the detector is 0.5 m/s, and the operating pressure is 3 MPa. Among the manual defects in the experimental field, width and length range from 10 to 60 mm, and depth varies from 1 to 9 mm. The defect regions are distributed manually. Some are located at two clock orientation, and some are connected together considered as MDR. All defects are associated with various types of local metal loss. There are 20 pipelines in the experimental field, which include 1280 IDRs with 800 UDMs and 480 BDMs. In addition, there are 65 MDRs. Examples of IDR and MDR. A. Evaluation Definitions The criteria introduced in [27] are referred to evaluate defect detection accuracy. Given a classifier and an instance, there are four possible outcomes. If the instance is defect and it is classified as defect, it is counted as a tr ue posi ti ve (T P), and if it is classified as nondefect, it is counted as f alse negati ve (F N). If the instance is nondefect and it is classified as nondefect, it is counted as a tr ue negati ve (T N), and if it is classified as defect, it is counted as a f alse posi ti ve (F P). The Pr eci si on and Recall as the first evaluation criterion are defined as TP × 100% (27) Precision = TP + FP TP × 100% (28) TP + FN Pr eci si on indicates the accuracy of defect detection, and (1 − Pr eci si on) is the false rate. Analogously, Recall indicates the comprehensiveness of defect detection, and (1 − Recall) is the miss rate. The high Pr eci si on and Recall indicate good detection performance. However, they are contradictory in some cases. For instance, if we just detect one outcome and confirm that the result is accurate, the Pr eci si on is 100% while Recall is low; if the algorithm outputs all Recall =
F=
(1 + α 2 ) × Precision × Recall × 100%. α 2 × Precision + Recall
(29)
α 2 = 0.3 [28] is applied in the proposed method. Pr eci si on, Recall, and F all together evaluate the detection accuracy. In order to further measure the accuracy of defect region determination, another evaluation B A described in (23) is also applied. B. Estimating Feature Parameters 1) Estimating Parameter of θs : As shown in Fig. 16, s ∈ {2, 4, 8, 16, 32}. The BA approximately rises first and then declines with each s, and the maximum value appears between 0.1 and 0.2 of s. Thus, the optimal θs∗ is set to 0.15. Meanwhile, the BA performs better in the case of s = 4 than other values of s, which is regarded as the optimal s1 of grid division in the process of anomaly inspection. 2) Estimating Parameters of θOC , θIC , and θCP : Estimated parameters of θOC , θIC , and θCP are shown in Fig. 17. The approximate optimal values are estimated by a Bayesian framework. Simultaneously, the estimate of two parameter components in each feature is displayed, respectively. The optimal parameter value is obtained by finding a parameter to maximize the distance of the blue curve and the red curve. C. Results 1) Results of Experimental Pipelines: a) Performance of the proposed method: In this section, the proposed method is evaluated by MFL measurements in experimental field. Different classifiers (SV M, R F, and K N N) are applied to detect the defects in the proposed process based on two stage. The comparisons of Pr eci si on, Recall, and F in 20 pipelines are shown in Fig. 18. R F and K N N have almost the same high Pr eci si on, Recall, and F values, while SV M shows highest Pr eci si on and lowest Recall. It can be seen that the values of three classic classifiers are above 80% with little difference, which verifies universality of the proposed method. The comparisons of B A are shown in Table I. Among different classifiers, R F has the highest B A. Although the BAs of SV M and K N N are lower than that of R F, B As for all three classifiers are over 80%, which shows that the proposed method can determine defect regions correctly. Detection accuracy of the special defect styles is discussed to further evaluate the proposed method. The detection accuracy of BDs is evaluated. The Recall of BDRs with different classifiers is shown in Table II, and the Recall of K N N can
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Fig. 17.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
Estimating parameters of θOC , θIC , and θCP . Estimation of: (a) θOCL ; (b) θOCC ; (c) θICL ; (d) θICC ; (e) θCPL ; and (f) θCPC .
Fig. 21. Fig. 18. Comparison results of Precision, Recall, and F with different classifiers.
Recall versus S N R with different feature extraction algorithms. TABLE II Recall OF BDR S W ITH D IFFERENT C LASSIFIERS
TABLE III S EGMENTATION R ESULTS FOR MDR S
Fig. 19.
Recall versus S N R for different classifiers.
Fig. 20. Comparison results of Precision, Recall, and F with three feature extraction algorithms.
reach 95%, which is greater than that of SV M with 83% and R F with 88%. In addition, the segmentation of M D Rs is also evaluated. The segmentation results are shown in Table III.
65 MDRs with a total of 169 defects in experimental pipelines are evaluated. R F gets the most number of estimated defects, and B A is higher than the other two classifiers. As shown in Table III, the proposed method can segment the MDRs into SDRs. Under complex corrosive conditions, the proposed method segments the MDRs successfully, which provides assurance for subsequent evaluation. In practice, MFL measurements contain many noises. Some small defects have low signal noise ratio (S N R), which makes them difficult to be detected. Thus, defects under different S N Rs are applied to evaluate the detection accuracy. In reality, the greater the noises defect regions have, the lower the S N R is. The Recall versus S N R of three classifiers is
LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
Fig. 22.
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Region determination process with Canny operator. (a) MFL image. (b) Region determination. (c) Regularization and extension.
TABLE V D ETECTION R ESULTS OF T EN I N -S ERVICE P IPELINES C ORRESPONDING TO N ATURAL C ORROSION
Fig. 23. B A results for 100 random defects with different methods. (a) B A distribution. (b) Statistical results.
Fig. 24. One pipe piece corresponding to natural corrosion. (a) Picture. (b) MFL measurements. TABLE IV S N R L EVEL OF D EFECTS U NDER D IFFERENT Recalls
shown in Fig. 19. The Recall increases with S N R. The greater S N R is, the higher Recall is. When S N R is over about 30 dB, Recall of different classifiers remains in a high level, which means that the proposed method have highest detection accuracy for defects with high S N R. In Table IV, the S N R level of defects under different Recalls is displayed. The proposed method can detect the defects with S N R over 10 dB to ensure Recall over 75%. Thus, the proposed method based on window features has a satisfied detection accuracy for defects under S N R of about 10 dB, which shows that the proposed method has a better detection accuracy of defects with low S N R.
b) Comparison and discussion: Under the same framework, the proposed method based on window features is compared with the feature extraction algorithms in [17] and [18]. An RF classifier in the proposed method is selected to be compared. The performance of detection with three feature extraction algorithms is shown in Fig. 20. Next, the detection accuracy of defects under different S N Rs is discussed in Fig. 21. Some observations are summarized as followed: 1) the proposed window features have the best detection accuracy over 80%, which is higher than traditional features in [17] and wavelet features in [18] 2) The greater S N R is, the higher value Recall is. All three methods have a high detection Recall for defects with high S N R, while Recall of the proposed method for defects with low S N R is higher than that of the other two methods. Canny operator is utilized to further evaluate the performance of defect region determination. Fig. 22 describes the region determination process with Canny operator. The irregular region after region determination is regularized with a rectangular region and expanded to two times large by manual experience. 100 defects in experimental field are randomly selected. The ability of region extraction is evaluated by B A. The comparison results are shown in Fig. 23. The B A distribution is shown in Fig. 24(a), and the statistical results by three B As are described in Fig. 24(b). B A of 100 defects is over 60% in the proposed method. In particular, B A of more than 80% of defects is above 80%. However, there are about 32% of defects in the method of [18] and Canny when B A is above 80%. It is just 3% in [17]. It is concluded that the performance of defect region determination with the proposed method is better than other methods. 2) Results of In-Service Pipelines: The proposed method is also applied for in-service pipelines with natural corrosion for defect identification. The in-service pipelines locate in northern China. This MFL testing was completed jointly by China’s Northeastern University, Beijing Huahang Radio Measurement & Research Institute, and Cnooc EnerTech Equipment
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 67, NO. 1, JANUARY 2018
Fig. 25. Comparison results of region determination with different methods for one pipe piece. (a) MFL image. (b) Manually marking. (c) Literature 17. (d) Literature 18. (e) Canny operator. (f) This article.
TABLE VI U NCERTAINTY E STIMATION OF THE P ROPOSED M ETHOD IN E XPERIMENTAL P IPELINES AND I N -S ERVICE P IPELINES
where the RF classifier is selected to be estimated. It can be seen that uncertainty of each evaluation is less than 1.5%, which shows good stability of the proposed algorithm. V. C ONCLUSION
Technology Co., Ltd. The length of pipelines is 88.99 km. The transmission medium in pipes is crude with 0.5% water ratio. Ten in-service pipelines with natural corrosion are chosen to evaluate the proposed method. The comparison results of defect detection in Table V show that the proposed method has the best detection accuracy. Among all evaluated in-service pipelines, the picture of one pipe piece is shown in Fig. 25(a), and corresponding MFL measurements are shown in Fig. 25(b). Region extraction results of different methods are shown in Fig. 25, where the MFL image is used to show performance. Regions of object defects are marked manually. The red rectangular boxes indicate the determined regions. 3) Uncertainty Estimation: Considering influences from sampling, noise, and algorithm itself, uncertainty estimation is very important to assess the significance of the proposed algorithm based on the “Guide to the Expression of Uncertainty in Measurement” [29]. Type A uncertainty is applied to estimate the proposed algorithm in this paper, and μ is estimated by calculating the standard deviation of the mean (X) of n number of independent observations X i at each of the same measurement conditions. Uncertainty can be computed by
n 2 1 i=1 (X i − X ) . (30) μ= √ n−1 n It is noticed that the results in experimental pipelines and in-service pipelines are average level of ten time experiments. Based on those, uncertainty results are shown in Table VI,
This paper deals with the defect identification of MFL testing for pipeline inner inspection. First, a novel feature set is proposed. These features include information inside and outside detected regions, which is helpful for defect detection and precise defect region determination. In addition, a Bayesian framework is used to estimate parameters of the proposed features. Second, a complete two-stage defect identification process is established, where the segmentation of complicated defect regions is seriously considered to further determine precise defect regions. The experimental results show that the proposed method performs well in defect identification, which realize the accuracy defect detection and precise defect region determination. In the future work, we need to further improve the accuracy and speed of the algorithm in practice. R EFERENCES [1] A. Foudazi, M. T. Ghasr, and K. M. Donnell, “Characterization of corroded reinforced steel bars by active microwave thermography,” IEEE Trans. Instrum. Meas., vol. 64, no. 9, pp. 2583–2585, Sep. 2015. [2] A. Foudazi, C. A. Edwards, M. T. Ghasr, and K. M. Donnell, “Active microwave thermography for defect detection of CFRP-strengthened cement-based materials,” IEEE Trans. Instrum. Meas., vol. 65, no. 11, pp. 2612–2620, Nov. 2016. [3] P. Wang, Y. Gao, G. Tian, and H. Wang, “Velocity effect analysis of dynamic magnetization in high speed magnetic flux leakage inspection,” NDT E Int., vol. 64, pp. 7–12, Jun. 2014. [4] Y. Zhang, Z. Ye, and X. Xu, “An adaptive method for channel equalization in MFL inspection,” NDT E Int., vol. 40, no. 2, pp. 127–139, Mar. 2007. [5] M. Li and D. A. Lowther, “Topological sensitivity analysis for steady state eddy current problems with an application to nondestructive testing,” IEEE Trans. Magn., vol. 47, no. 5, pp. 1294–1297, May 2011.
LIU et al.: WINDOW FEATURE-BASED TWO-STAGE DEFECT IDENTIFICATION USING MFL MEASUREMENTS
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Jinhai Liu (M’09) received the B.S. degree in automation from the Harbin Institute of Technology, Harbin, China, in 2002, and the M.S. degree in power electronics and power transmission and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2005 and 2009, respectively. He is currently an Associate Professor and a Doctoral Supervisor with Northeastern University. His current research interests include data driven fault diagnosis, industrial big data analysis, and safety technology of long pipelines.
Mingrui Fu (S’17) received the B.S. degree from Northeast Forestry University, Harbin, China, in 2010. He is currently pursuing the Ph.D. degree with the Institute of Information Science and Engineering, Northeastern University, Shenyang, China. His current research interests include data driven fault diagnosis, industrial big data analysis, and safety technology of long pipelines.
Feilong Liu (SM’14) received the B.S. degree from Northeastern University, Shenyang, China, in 1995, the M.S. degree from the South China University of Technology, Guangzhou, China, in 2000, and the Ph.D. degree from the University of Southern California, Los Angeles, CA, USA, in 2008, all in electrical engineering. He is currently an Adjunct Professor with Northeastern University and is actively involved in applying signal processing, pattern recognition, artificial intelligence, computational intelligence, statistics, and optimization techniques.
Jian Feng (M’16) received the B.S., M.S., and Ph.D. degrees in control theory and control engineering from Northeastern University, Shenyang, China, in 1993, 1996, and 2005, respectively. He is currently a Professor, a Doctoral Supervisor, and the Vice Head of the Electric Automation Institute, Northeastern University. His current research interests include fault diagnosis, signal processing, and neural networks.
Kuangqing Cui received the B.S. degree from Dalian Maritime University, Dalian, China, in 2006. He is currently the Manager of the Technology Department, CNOOC EnerTech Equipment Technology Co., Ltd., Tianjin, China. His current research interests include magnetic flux leakage detection technology, pipeline integrity management, ultrasonic testing technology, pipeline status assessment, and corrosion analysis.
