quantitativenondestructivetestingofwireropeusingimage...

Research ArticleQuantitative Nondestructive Testing of Wire Rope Using ImageSuper-Resolution Method and AdaBoost Classifier

Jigang Li 12 and Juwei Zhang 12

1College of Electrical Engineering Henan University of Science and Technology Luoyang 471023 China2Power Electronics Device and System Engineering Laboratory of Henan Henan University of Science and TechnologyLuoyang 471023 China

Correspondence should be addressed to Juwei Zhang juweizhang163com

Received 17 June 2019 Revised 6 July 2019 Accepted 25 July 2019 Published 4 August 2019

Academic Editor Angelo Marcelo Tusset

Copyright copy 2019 Jigang Li and Juwei Zhang -is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Magnetic flux leakage (MFL) detection is commonly employed to detect wire rope defects However nondestructive testing(NDT) of a wire rope still has problems A wire rope nondestructive testing device based on a double detection board is designed tosolve the problems of large volume complex operations and limited circumferential resolution due to sensor size in traditionaldevices -e device adopts two magnetic sensor arrays to form the double detection board and collects the MFL data of themagnetized wire rope-ese sensors on the double detection board are staggered and evenly arranged on the circumference of thewire rope A super-resolution algorithm based on interpolation uses non-subsampled shearlet transform (NSST) combiningprincipal component analysis (PCA) and Gaussian fuzzy logic (GFL) and fuses the data of double detection board to improve theresolution and quality of defect images Image quality measurement and comparison experiments are designed to verify that defectimages are effectively enhanced An AdaBoost classifier is designed to classify defects by texture features and invariant moments ofdefect images -e experimental results show that the detection device not only improves the circumferential resolution but alsothe operation is simple the resolution and quality of the defect images are improved by the proposed super-resolution algorithmand defects are identified by using the AdaBoost classifier

1 Introduction

Wire ropes are widely used in industrial applications such ascoal mining and other industrial applications because oftheir advantages good flexibility high strength and strongbearing capacity -e safe operation of wire rope affects thesafety of industrial production and personnel Detecting thedamage of wire ropes has important social and economicbenefits -e nondestructive testing of a wire rope includeselectromagnetic detection ultrasonic detection optical de-tection X-ray detection and acoustic emission detectionmethods -e electromagnetic detection method has widelybeen used and promoted in current research and applica-tion It includes magnetic flux leakage (MFL) detection eddycurrent detection magnetic memory detection and mag-netic particle detection MFL detection is the most com-monly used method due to its simple structure and strong

applicability -e principle of MFL detection is based on thedistribution of leakage magnetic field generated by themagnetized wire rope -e damage of the wire rope willaffect the magnetic flux leakage distribution on the surfaceand the damage detection can be realized by measuring theleakage magnetic field [1]

In the MFL detection method the magnetizationmethod of the wire rope includes coil magnetization [2 3]and permanent magnet magnetization [4 5] Sun et al [2]proposed an opening electric magnetizer based on themagnetic control for a C-like electric loop-coil Its mag-netization effect was confirmed by simulations and exper-iments with the designed 3-D models and prototypes Wuet al [3] combined Helmholtz-like coils and a custom-mademagnetic shield to design the electromagnet magnetizer Itsstructural parameters were optimized by the orthogonal testmethod Coil magnetization method requires current flow

HindawiShock and VibrationVolume 2019 Article ID 1683494 13 pageshttpsdoiorg10115520191683494

into multiple coils to realize magnetization which will leadto problems of coil heating and complicated operationPermanent magnet magnetization is easy to operate Andpermanent magnets are usually uniformly arranged aroundthe circumference of the wire rope for magnetization Sunet al [4] proposed an open permanent magnet magneti-zationmethod which solved the problem of strongmagneticforce and large weight in the yoke magnetic method Yu et al[5] considered the effects of excitation system on the per-formance of leakage magnetic nondestructive testing andoptimized the dimensions of the yoke Wang et al [6]combined the structural models for dynamic magnetic fieldbalancing and magnetic focusing which effectively reducedthe interference produced by the interactions between theenvironmental magnetic fields and the wire rope strand Panet al [7] proposed a simple and portable magnetic detectordevice which was very light and easy to design andmanufacture

-e leakage magnetic field on the surface of the wire ropeis usually collected by magnetic sensors which includes Hallsensor [8] coil sensor [9] fluxgate sensor [10] giantmagnetoresistance (GMR) sensor [11] tunnel magnetore-sistance (TMR) sensor [12] -e circumferential resolutionand sensitivity of magnetic sensors array are important forthe detection of wire rope damage -e circumferentialresolution of magnetic sensor array affects the location ofwire rope damage Kim and Park [8] designed a hall sensorarray to detect wire rope damage the MFL signal at thedefect was enveloped and compared with the threshold toquantify the defect However the low circumferential res-olution due to Hall sensor size made it difficult to accuratelylocate the circumferential position of the damage And thesensitivity of the magnetic sensor also affects the effective-ness of detecting damages Yan et al [9] designed a kind ofiron core as coil winding skeleton and their design im-proved the signal-to-noise ratio (SNR) of magnetic fluxleakage signal and simplified coil sensor Lei et al [11]proposed a detection device based on high-sensitivity GMRsensors which solved the problem of low SNR caused by thesmall diameter and large lift off Liu et al [12] designed acircular TMR-based MFL sensor for slight wire rope flawdetection and detected accurately the axial and circumfer-ential positions of these broken wire flaws

After the magnetic sensors obtain the leakage magneticfield on the surface of the wire rope signal processing andimage processing techniques are used to achieve qualitativeand quantitative detection of defects -e MFL signal of thewire rope contains various background noises -e signalprocessing methods are used to filter out these noises whichcan achieve the qualitative analysis of defects Zhang et al[13] designed a wavelet filtering algorithm combining theHilbertndashHuang transformation with compression sensingwhich effectively suppress the system noise Wang [14] usedthe wavelet method to analyze the magnetic flux leakagesignal at the defect and verified the wavelet base db4 with thebest noise reduction effect Liu et al [15] proposed a wirerope detection signal processing method combining notchfilter and wavelet denoising which could effectively dis-tinguish wire rope defect signal and strand signal with high

detection accuracy even for the inner defect Image pro-cessing technology can visualize the magnetic flux leakagesignal of defects and improve the quality and resolution ofdefect images which is important for the quantitativeanalysis of defects Zhao et al [16] used circumferentialinterpolation to improve the circumferential resolution ofthe original MFL images and achieved good detection re-sults Zhang et al [17] proposed wavelet super-resolutiontechnology to improve the resolution of the defect imagemaking the edge information of the defect more obvious andeasier to identify Tan and Zhang [18] proposed a super-resolution reconstruction method based on Tikhonov reg-ularization to enhance defect image and verified that imageproperty was the best when super-resolution result wastriple However this method did not apply the exact imagequality measurement experiments to verify the effect ofdefect image enhancement In addition to these imageprocessing techniques to improve resolution some imagefusion methods can also be used to enhance the MFL imageof wire rope defects In the field of image fusion traditionalmultiscale image representation methods have pyramidwavelet curvelet and shearlet Among them discretewavelet transform is a common method but it has somedisadvantages such as not shift invariance poor di-rectionality and not a time-invariant transform [19]Compared to the wavelet transform contourlet transformhas good multiresolution and directionality but it lacks shiftinvariance Non-subsampled contourlet transform is pro-posed to solve this problem which adds shift invariance tothe contourlet transform and it can better express the edgeand texture information of the image [20] However it is notefficient and takes a long time To solve this problem NSST isproposed It not only has short running time and can meetthe requirements of real-time [21] but also can extract moredetailed features of the target image in different directions

In order to solve the problems of large volume complexoperations and limited circumferential resolution due tosensor size in traditional devices a MFL detection devicebased on the double detection board is designed -e doubledetection board consists of magnetic sensors and thesesensors are staggered and evenly arranged on the circum-ference of the wire rope -e wire rope is magnetized bypermanent magnets and the double detection board collectsthe surface MFL data of the wire rope A super-resolutionalgorithm based on interpolation is applied to double theresolution of defect images NSST is applied to super-res-olution images which are decomposed into high-frequencycoefficients and low-frequency coefficients For high-fre-quency coefficients PCA is implemented as a fusion ruleFor low-frequency coefficients GFL is implemented as afusion rule After fusing these coefficients inverse NSST isapplied to reconstruct the fused image -e proposed al-gorithm fuses the data of double detection board to improvethe quality of defect images Various image quality mea-surement and comparison experiments are performed andthe results show that spatial resolution of defect images isenhanced effectively and high-quality images are obtainedImage descriptions of texture features and invariant mo-ments are extracted as the feature vector of the defect which

2 Shock and Vibration

are the input of the AdaBoost classifier and are used toidentify the defects

2 Experimental Design

21 Experimental Platform -e principle of the whole ex-periment is based on the unsaturated magnetic excitationdetection method Under the excitation state magnetic fieldlines generated by the permanent magnet pass through theair inside the excitation rope to form magnetic loops andthere are weakMFL signals at defect as shown in Figure 1(a)Defect information is analyzed by these weak MFL signalsBecause the unsaturated magnetic excitation detectionmethod can obtain smoother defect flux leakage signals thanthe remanence detection method [18] it is applied in thispaper More details about unsaturated magnetic excitationmethods can also be found in [18]

-e principle of the double detection board is to obtainmore circumferential magnetic flux leakage informationthrough the two detection boards A single detection boardlimits the number of sensors that can be accommodated dueto the sensor size which also limits the collection of cir-cumferential MFL information of the wire rope Two de-tection boards are interlaced around the wire rope whichcan collect more circumferential MFL information than onedetection board Double detection board combines withunsaturated excitation detection method constituting thewhole experimental platform

-e whole experimental platform is designed as shownin Figure 1(b) -e system mainly includes unsaturatedmagnetic excitation module double detection board en-coder data storage and control system

-e unsaturated magnetic excitation module contains 12permanent magnets Permanent magnet material is NdFeBremanence strength is 118 Tesla andmagnetization distanceis 15 cm Multiple elongated permanent magnets are evenlydistributed around the circumference of the wire rope andthe wire rope is in the excited state -e double detectionboard consists of two sensor arrays as show in Figure 2 Andeach sensor array is made of 18 giant magnetoresistance(GMR) sensors -e number of sensors is determined by thelift off distance and sensor size [18] In order to ensure thattwo sensor arrays do not repeatedly collect the MFL data onthe surface of the wire rope the circumferential angle of thesensor is θ (θ10deg) and the axial distance between the twosensor arrays is L (L is set to 1 cm) as shown in Figure 2 Asshown in Figure 3 these sensors on the double detectionboard are staggered and evenly arranged on the circum-ference of the wire rope -e encoder generates controlpulses to guarantee the equal spatial sampling In additionan ARM chip is used as the control chip and secure digital(SD) memory card is used for the data storage

22 Experimental Flow -e acquisition processes of thedouble detection board are as follows the wire rope ismagnetized by the excitation module the double detectionboard and the encoder are connected with the controlsystem and entire acquisition system is loaded onto the

magnetized wire rope When the encoder moves along thewire rope axis it sends out pulse signal According to theencoder pulse signal the double detection board is con-trolled to collect MFL data on the wire rope surface -ere isan interval between the double detection board which willcause the collected data to be of the same length but thecollected data on the surface of the wire rope are notcompletely coincident -erefore the MFL data of thedouble detection board cannot be simply superimposedtogether for subsequent processing In order to improve theresolution of defect images by fusing the data of doubledetection board data of the double detection board aredivided into the first board data and the second board data-e double detection board is grouped for data processingand image processing to realize the quantitative identifi-cation of wire rope defects and the processing steps areshown in Figure 4

3 Data Processing

In the experiment the diameter of steel wire rope is 28mmand the structure is 6times 37 and a total of 222 steel wires wereused Broken wires are the main form of wire rope breakageand it is more difficult to identify the broken wires with smallspacing so it is moremeaningful to identify the broken wireswith small spacing Artificial defect types included onediscontinuity to five and seven broken wires as shown inFigure 5 And each defect is destroyed as small gap (about02 cm)

MFL data on the surface of wire rope collected by theexperiment are shown as Figure 6 -e original MFL datacontain a lot of system noise -ese noises include high-frequency magnetic flux leakage noise caused by unevenexcitation between sensor channels baseline drift caused bychange of lift off namely low-frequency noise and wavenoise caused by spiral structure of the wire rope -ese noiseswill affect the subsequent defect location and quantitativeidentification results as well as the repeatability of the resultsIn order to suppress these noises an effective noise reductionalgorithm is needed Wavelet analysis has widely been used indigital signal processing In this section the data processingmainly includes the wavelet soft threshold denoising algo-rithm for the wire rope MFL signal processing

Wavelet analysis [14 15] has been well applied in wirerope MFL signal processing and proved to be an effectivesignal processing method In this paper wavelet analysis isused to decompose the MFL signal of the wire rope and thehigh- and low-frequency coefficients of the signal are ob-tained in which the high-frequency coefficient contains high-frequency magnetic flux leakage noise and wave noise and thelow-frequency coefficient is the baseline -e high-frequencycoefficient is treated by soft threshold and the low-frequencycoefficient is cleared -e proposed algorithm is as follows

(1) Select the n-th board MFL data (n 1 2)(2) Using db4 wavelet to decompose the data d(i) of a

sensor channel with 7 level (i 1ndash18) the data areshown in Figure 7 and wavelet decomposition is asfollows

Shock and Vibration 3

xAj+1 1113944 ho(n minus 2k)xAj

xDj+1 1113944 hl(n minus 2k)xAj

⎧⎪⎨

⎪⎩(1)

1113954xAj 1113944 ho(k minus 2n)xAj+1

+ hl(k minus 2n)xDj+1 (2)

where ho is the low-pass filter hl is the high-passfilter and ho(k) hl(minus k) xAj+1

is the j-th low-fre-quency coefficient xDj+1

is the j-th high-frequencycoefficient and 1113954xAj

is the reconstructed signal(3) -e low-frequency wavelet coefficient is cleared(4) -e high-frequency coefficients of each de-

composition layer are processed by wavelet softthreshold the universal threshold is ldquominimaxirdquo

(5) -e processing wavelet coefficients are reconstructedby using the reconstruction equation (2) with whichthe denoising data are obtained as shown in Figure 8

4 Image Processing

-e procedure of image processing in this section mainlyincludes the image preprocessing and image enhancement-e MFL data collected by the double detection board aregrouped into the first board data and the second board data-e first board data are processed by data processing andimage preprocessing to obtain a defect image -e secondboard data are also processed by the same to obtain anotherdefect image -e two defect images show the same defectbut they represent different information of the same defect-e quality of these two images is not good enough and theresolution is not enough -ese two images are processed byimage enhancement which fuses different information toobtain a defect image with higher resolution and betterquality

41 Image Preprocessing -e image preprocessing mainlyincludes the defect location and segmentation the gray-scalenormalization and circumferential interpolation -e loca-tion and segmentation of defects were performed by themodulus maximamethod [17]-emodulus maximamethodis used to obtain the axial position information of the defectregions -e defect images are segmented according to theaxial information of the defects region -e gray-scale nor-malization can transform defect images into defect gray

images Defect images are normalized to [0 255] by the max-min normalization method which converts MFL data intodefect image Each sensor array in the double detection boardhas 18 channels -erefore the circumferential resolution ofdefect image is only 18 which is far below the axial resolutionIn order to make the defect image more intuitive cubic splineinterpolation method is used to improve the circumferentialresolution from 18 to 300

After the above image preprocessing two images of thesame defect are finally obtained -e specific process ofimage preprocessing is as follows

(1) Select the n-th board data after noise reduction(n 1 2) as shown in Figure 9

(2) Set the threshold to obtain the channel where thedefect is located sum up all defect channels and

Wire ropeNS

(a)

Double detection board Encoder

Wire rope

Detection direction

Permanent magnet

(b)

Figure 1 (a) -e principle of the unsaturated magnetic excitation detection method (b) Data acquisition platform schematic

Wire rope

θ

L

Figure 2 Double detection board schematic

Figure 3 Double detection board


take the absolute value where the position with themaximum value is the axial coordinates of thedefect

(3) -e 18times 300 defect images is segmented according tothe axial coordinates of the defect which are

centered on these center points and the defect re-gions are shifted to the center along the circum-ferential direction of the defect images

(4) -e defect images are normalized to [0 255] toobtain the defect gray images

First boarddata

Second boarddata

Signal denoise

Signal denoise Image locate extractand normalize

Image locate extractand normalize Defect image

enhancementExtract

feature vectorClassificationrecognition

Imageprocessing

Dataprocessing

Double boarddata collection

Figure 4 Defect recognition flow chart

(a) (b)

(c) (d)

(e) (f )

Figure 5 Images of broken wires (a) One broken wire (b) Two broken wires (c)-ree broken wires (d) Four broken wires (e) Five brokenwires (f ) Seven broken wires

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(a)

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(b)

Figure 6 -e original MFL data (a) -e first board (b) -e second board


(5) Cubic spline interpolation is used to improve thecircumferential resolution of defect gray imagesfrom 18 to 300

(6) -e 300times 300 defect gray images of the same defectare finally obtained as shown in Figure 10

42 ImageEnhancement In this section image enhancementmainly uses the super-resolution algorithm-based in-terpolation to fuse two images of the same defect -e al-gorithm not only improves the resolution of the defect imagebut also improves the quality -e flow chart of proposedalgorithm is shown in Figure 11 High-resolution and high-quality images can provide more details about defects Moredetails can improve the distance between the characteristics ofthe defect images making the defect image easier to classifyimproving the accuracy of quantitative recognition

-e super-resolution method can transform the low-resolution image into the high-resolution image and haswidely been used in image enhancement Image interpolationis widely used in image super-resolution due to its simplicityand speed Traditional interpolation methods include nearestneighbor interpolation bicubic interpolation and bilinearinterpolation Among them bicubic interpolation is the bestbecause bicubic interpolation can produce smoother edgesthan the others [22] In this paper the super-resolutionmethod based on bicubic interpolation is used to improve the

resolution of two images of the same defect -e resolution ofeach image is doubled which can continue to increase butthis situation will increase the computational cost

After obtaining two high-resolution images at the samedefect NSST is used to fuse two high-resolution images at thesame defect And NSST theory is explained in Section 421NSST is applied to two high-resolution images at the samedefect separately Each image is decomposed into its corre-sponding high-frequency and low-frequency coefficients-enthe fusion of the high-frequency coefficients and the low-fre-quency coefficients of the two pictures are respectively per-formed by different fusion rules-e high-frequency coefficientrepresents the details of image PCA is used as fusion rule Andthe rule is explained in Section 422 -e low-frequency co-efficients represent the contour of the image and GFL is used asfusion rule and is explained in Section 423 After fusing thecoefficients inverse NSST is applied to reconstruct the fusedimage Because of the super-resolution method the size of thefused image is bigger than the original defect image Tomeasurequality objectively the fused image is resized to the size oforiginal source image using interpolation-based resizingmethod And finally the fused image with the original size iscreated and is ready for image quality measurement

421 Non-Subsampled Shearlet Transform NSST uses non-subsampled pyramid filters (NSPFs) to decompose the inputimage into different scales If image is decomposed in L-level L+ 1 subbands of the same size as the input image willbe obtained including L high-frequency subbands and onelow-frequency subband For each decomposition level shift-invariant shearlet filter banks (SFBs) are used to decomposesubbands into different directional subbands More detailsabout NSST can be found in the literature [23] Due to thecharacteristics of NSST such as multiscale multidirectionand shift invariance it is selected as the proposed fusionmethod in this paper

422 Principal Component Analysis PCA can convert alarge number of related variables into unrelated variables-at means this method can reduce the redundant data andextract the important parts of images so it is widely used inthe field of image fusion PCA uses a weighted average ofimages to fuse these source images and the weights dependson the eigenvector corresponding to the largest eigenvalue ofthe covariance matrices of each source image And PCA isselected as fusion rule of high-frequency coefficients in thispaper More details about PCA can be found in the literature[24] -e PCA algorithm steps are shortly defined as follows

(1) Let the source images (images to be fused) bearranged in two-column vectors

(2) Subtract the mean of each column from the two-column vectors

(3) Calculate covariance matrixes of the two-columnvectors

(4) Calculate eigenvectors V of covariance matrixes andV is diagonal matrix with dimension 2 times 2

0

500

1000

1500

2000

2500

Am

plitu

de

32 50 1 4Axial distance

Figure 7 -e original MFL data of single channel

ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de

1 2 3 4 50Axial distance

Figure 8 -e denoising data of single channel


(5) Consider eigenvalues of V which correspond to V(1)

and V(2) to compute p1 and p2 as

p1 V1

1113936 V

p2 V2

1113936 V

(3)

-e fusion rule about PCA is as follows

fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)

where A and B respectively denote source images 1 and 2and fd

Ak and fdBk are the high-frequency subimages of

source images 1 and 2 respectively

423 Gaussian Fuzzy Logic GFL has been well applied inimage fusion -e low-frequency coefficient of the sourceimage contains the target information and backgroundinformation of the image GFL can select the feature targetinformation of the source image and complement thebackground information of another source image By usingthe weighted average method based on GFL to fuse the image

(a) (b)

(c) (d)

(e) (f)

Figure 10-e two gray-scale images of the same defect (a) One broken wire (b) Two broken wires (c)-ree broken wires (d) Four brokenwires (e) Five broken wires (f ) Seven broken wires

0

12001000

800600400200

ndash200ndash400ndash600ndash800

05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)

Figure 9 -e original denoising wire rope MFL image (a) -e first board (b) -e second board


low-frequency coefficients the main information and contourinformation of the source image can be retained to the greatestextent So GFL is selected as fusion rule of low-frequencycoefficients in this paper More details about GFL can be foundin the literature [25] GFL formulas are as follows

η0 exp minusfA(i j) minus μ( 1113857

2

2(kσ)21113888 1113889

η1 1 minus η0

f(i j) η0fA(i j) + η1fB(i j)

(5)

where μ and σ respectively are the mean and variance of thesource image 1 k is a constant and is set to 15 and fA and

fB are the low-frequency subimages of source images 1 and2 respectively

43 Image Quality Measurement and Comparison Since thedefect gray image is relatively simple and intuitive it is not easyto see the quality changes before and after the enhancement ofthe image quality and resolution Tomeasure the visual effect ofthe enhanced image eight metrics are applied to make acomprehensive evaluation -ese evaluation measures includeaverage gradient [25] information entropy [25] standarddeviation [25] space infrequency [25] mutual information[26] Petrovics metric [26] signal-to-noise ratio [24] andstructural similarity index measure [24] as follows

431 Image Quality Measurement Image quality mea-surement experiment is performed to prove the effectivenessof the proposed super-resolution algorithm for defect im-ages enhancement In the experiment images with improvedresolution and two source images were used for qualitymeasurement Four groups of quality measurement in-dicators were selected as shown in equations (6)ndash(10) andeight groups of different images were measured in eachgroup -e experimental results are shown in Tables 1ndash4and the average results in these tables are shown in Table 5

Since the resolution of the image improved by the al-gorithm is inconsistent with that of the source image whichaffects the quality measurement comparison the image withthe improved resolution is adjusted to the size of the sourceimage and the adjusted image is used for experiments Inthese tables B1 and B2 represent source images 1 and 2respectively as shown in Figure 11 and the two sourceimages represent the data collected by double detectionboards respectively And B3 represents the adjusted image

(1) Average gradient (AG)

AG 1

m times n

1113936miminus 11113936

njminus 2(F(i j) minus F(i j minus 1))1113872 1113873

2+ 1113936

miminus 21113936

njminus 1(F(i j) minus F(i minus 1 j))1113872 1113873

2

2

1113971

(6)

where F denotes the final adjusted image whose sizeis m times n

(2) Information entropy (IE)

IE minus 1113944L

i0p(i) log2(p(i)) (7)

where p(i) denotes the probability of pixels whosegray value amount to i over the total image pixels

(3) Standard deviation (SD)

SD

1m times n

1113944

m

iminus 11113944

n

jminus 1F(i j) minus

1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)

(4) Space infrequency (SF)

SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n

j1[F(i j) minus F(i minus 1 j)]

2

11139741113972

CF

1m times n

1113944

m

i11113944

n

j2[F(i j) minus F(i j minus 1)]

2

11139741113972

(10)

where RF and CF respectively denote the row fre-quency and column frequency

Source image 1 Source image 2

Super-resolution method

Non-subsampled shearlet transform

Low-frequencycoefficient

High-frequency coefficient


High-frequencycoefficient

Gaussian fuzzy logic Principal component analysis



Inverse non-subsampled shearlet transform

Fused image

Resize fused image to original size

Figure 11 Flow chart of the proposed algorithm


-e results from Tables 1ndash5 especially Table 5 show thatall four quality measures of B3 exceed those of B1 and B2which means that the size adjusted images are of betterquality than the two source images Experimental resultsshow that the proposed super-resolution algorithm caneffectively improve the quality and resolution of defectimages

432 Image Quality Comparison Image quality compar-ison experiment is to prove the feasibility of the proposedsuper-resolution algorithm for defect image enhance-ment -e performance of the proposed algorithm iscompared with several algorithms bicubic interpolation(BI) wavelet super-resolution reconstruction (WSR)[17] stationary wavelet transform super-resolutionmethod (SWTSR) [26] and non-subsampled contourlettransform super-resolution reconstruction (NSCTSR)[27]

Two source images were used to test these super-resolution algorithms and their super-resolution ren-derings are shown in Figure 12 It is not easy to see thedifference of image quality from these super-resolutionresults which is due to the simple structure and texture ofdefect gray-scale images -erefore four measurementindexes were used to measure these super-resolutionresult images -e four measurement indicators are asshown in equations (11)ndash(17) Various super-resolutionmeasurement results are shown in Table 6

(5) Mutual information (MI)

MI MIAF + MIBF (11)

MIXF 1113944xf

pXF(x f) logpXF(x f)

pX(x)pF(f) (12)

where A and B respectively denote source images1 and 2 X is A or B and pXF is the normalized gray

Table 1 AG

Group 1 2 3 4 5 6 7 8 AverageB1 00035 00029 00024 00040 00025 00071 00038 00031 00037B2 00035 00031 00024 00033 00028 00055 00045 00056 00038B3 00039 00035 00031 00042 00034 00075 00053 00041 00044

Table 2 IE


Table 3 SD


Table 4 SF


Table 5 -e average results

Measure AG IE SD SFB1 00037 39850 164246 15490B2 00038 40520 155980 16199B3 00044 40987 171959 18484


histograms of these source images and the fusionimage respectively

(6) Petrovics metric (QABF)

QABF

1113936

Ni11113936

Mj1Q

AF(i j)wA(i j) + QBF(i j)wB(i j)

1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)

where QAF shows the relation with source image1 and fused image F looking to edge informa-tion And wA shows the edge strength of sourceimage 1

(7) Signal-to-noise ratio (SNR)

SNR SNRA + SNRB (14)

SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936

Nj1 fX(i j) minus f(i j)( 1113857

2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)

(8) Structural similarity index measure (SSIM)

SSIM SSIMAF + SSIMBF1113872 1113873 (16)

SSIMXF 2uxuf + C11113872 1113873 2σxσf + C21113872 1113873

u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)

-e results from Table 6 show that all four qual-ity measures of B3 exceed those of B1 and B2 whichmeans that the proposed super-resolution algorithmhas a good effect on gray-scale image enhancementof wire rope defects Experimental results show thatthe proposed algorithm is feasible to wire rope defectimage enhancement

5 Quantitative Identification

Quantitative identification is an important goal of wire ropenondestructive testing In this part texture features and

(a) (b) (c)

(d) (e) (f )

Figure 12 Various super-resolution results (a) Two source images (b) BI (c) WSR (d) SWTSR (e) NSCTSR (f ) Proposed

Table 6 Various super-resolution measurement results

Measure MI QABF SNR SSIMBI 40838 06030 00198 06163WSR 51995 05929 00011 06207SWTSR 41538 05907 00064 05934NSCTSR 39762 05573 00021 04914Proposed 53348 06430 00403 06313


seventh-order moment invariant features of defect imagesare extracted as feature vectors of defect images -esefeatures include standard deviation smoothness third-or-der moment consistency and entropy and the first thirdfifth and seventh moments of seven-order invariant mo-ments -e designed AdaBoost classifier recognizes thesedefects by the feature vectors of defect images

51 AdaBoost Classifier -e adaptive boosting (AdaBoost)ensemble is to combine a number of weak classifiers to geta strong classifier which has better classification effectEach weak learner is a simple classifier such as decisiontree and neural networks AdaBoost combines weaklearners such as decision trees to make it one of the bestclassifiers

AdaBoost is a classifier with high accuracy It is simpledoes not require feature screening and does not worry aboutoverfitting -e flow chart for AdaBoost is as shown in

Figure 13 In this paper the weak classifier selects the de-cision tree AdaBoost is divided into training process andtesting process In the process of training the trainingsamples are set as the same initial weights a weak classifier istrained and the classification error rate is calculated -enthe weight values are updated iteratively in each iterationbased on the previous classification result that is increasethe sample weight of wrong classification and reduce theweight of correctly classified samples If classification errorrate is more than or equal to 05 the weight will be reini-tialized Each weak learner has weight which is proportionalto the classification error rate In the process of testing thetest samples are used for these weighted classifiers and thefinal classification results are output More details aboutAdaBoost can be found in the literature [28]

52 Statistics Results Broken wires are the main damageform of wire rope and small spacing of broken wires is

Training data

Weak classifier 1

Ensemblemodel

Weighted combination

Testing data

PredictionWeak classifier 2

Weak classifier 3

Weak classifier n

Training process Testing process

Figure 13 AdaBoost classifier flow chart

03040506070809

1

Reco

gniti

on ra

te

05 1 15 2 25 30Error percentage

X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)

Figure 14 Identification results of broken wires under different decision trees (a) 10 (b) 20 (c) 40 (d) 60


difficult to identify and more meaningful In the quantitativeidentification experiment 125 samples of concentrated bro-ken wires were manufactured manually with a small spacingof about 02 cm and the types of broken wires included 1 to 5and 7 -e broken wire samples were randomly divided intotraining samples and test samples -e number of trainingsamples was 94 (about 75) and the number of test sampleswas 31 (about 25) In this paper the number of broken wiresidentified by AdaBoost classifier is converted into the per-centage of broken wires it represents the percentage ofbroken wires in the total wires and makes the classification ofbroken wires more intuitive As shown in Figure 14 theidentification result graph under different number of decisiontrees has the best recognition effect when the number ofdecision trees is 60 When the permissible error of brokenwires is 045 which means the permissible error is one wirethe recognition rate of broken wires reaches 9355 and themaximum error was not more than 09

6 Results and Discussion

In this paper a wire rope nondestructive testing device basedon the double detection board is designed to collect MFL dataof the wire rope -e double detection board can collect morecircumferential information of the wire rope surface A super-resolution algorithm combining interpolation and NSST isused to improve the resolution and quality of defect images-e interpolation algorithm uses cubic interpolation to im-prove the resolution of defect images NSSTdecomposes thesehigh-resolution images to get high-frequency and low-fre-quency images and GFL fuses low-frequency images andPCA fuses high-frequency images -e super-resolution al-gorithm fuses the data of the double detection board toproduce better quality and higher resolution defect imagesVarious image quality measurements and comparison ex-periments are performed to show the effectiveness of theproposed algorithm Compared with the super-resolutionalgorithm in literature [17 26 27] the proposed algorithmhas better image quality improvement effect After obtaininghigh-resolution defect images with good quality the Ada-Boost classifier was designed to identify these defect images soas to achieve quantitative recognition of broken wires Whenthe permissible error of broken wire is 045 (the permissibleerror is one wire) the highest recognition rate of the brokenwire is 9355 In comparison with [18] the identificationaccuracy rate was 9143 with the permissible error of onewire Compared with [13] the accuracy was 9375 under apermissible error of two wires -e proposed method out-performs existing methods Quantitative identification resultsshow that the AdaBoost classifier is feasible and effective forbroken wires recognition

In the experiments the distance between the doubledetection board and the excitation source in the data ac-quisition platform is different which results in some differ-ences in the signal-to-noise ratio of the MFL data collected byeach detection board in the double detection board Using thesame filtering algorithm for the signals collected by the doubledetection board will lead to the difference of filtering effectwhich will affect the image super-resolution enhancement

effect Furthermore in the image preprocessing part cubicspline interpolation is used to improve the circumferentialresolution of the defect image while in the image enhance-ment part bicubic interpolation is used again to improve theresolution of the image and so repeated interpolation willaffect the defect image quality -erefore future research willfocus on the optimization of the filtering algorithm and imagesuper-resolution algorithm

7 Conclusions

In this paper the three research works have been performedFirstly a wire rope nondestructive testing device based onthe double detection board to address the disadvantages ofthe traditional detection device Compared with traditionalMFL traditional detection device the proposed device hassmall volume simple operations and high circumferentialresolution Secondly a super-resolution algorithm com-bining interpolation and NSST is used to fuse the MFL dataof double detection board to improve the quality of defectimages Various image quality measurements and com-parison experiments are performed to show the effectivenessof the proposed algorithm Finally the AdaBoost classifier isdesigned to identify the broken wires quantitatively -eresults of quantitative experiment show a good recognitioneffect of broken wires In the future the filtering algorithmand image super-resolution algorithm will continue to beoptimized and more types of damage will be studied

Data Availability

-e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

-e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

-is work was partially supported by the National NaturalScience Foundation of China (grant nos 6104001061172014 and U1504617) the Key Technologies RampDProgram of Henan Province (grant no 152102210284) theScience and Technology Program of Henan EducationDepartment (grant no 17A510009) and the Science andTechnology Open Cooperation Program of Henan Province(grant no 182106000026)

References

[1] J Tian J Zhou H Wang and G Meng ldquoLiterature review ofresearch on the technology of wire rope nondestructive in-spection in China and abroadrdquo MATEC Web of Conferencesvol 22 article 03025 2015

[2] Y Sun J Wu B Feng and Y Kang ldquoAn opening electric-MFL detector for the NDT of in-service mine hoist wirerdquoIEEE Sensors Journal vol 14 no 6 pp 2042ndash2047 2014

[3] B Wu Y J Wang X C Liu and C F He ldquoA novel TMR-based MFL sensor for steel wire rope inspection using the


orthogonal test methodrdquo Smart Materials and Structuresvol 24 no 7 article 075007 2015

[4] Y Sun S Liu and L He ldquoA new detection sensor for wirerope based on open magnetization methodrdquo MaterialsEvaluation vol 75 no 4 pp 501ndash509 2017

[5] C Yu J Jiao G Li X Liu C He and B Wu ldquoEffects ofexcitation system on the performance of magnetic-flux-leakage-type non-destructive testingrdquo Sensors amp Actuators APhysical vol 268 pp 201ndash212 2017

[6] H Wang J Tian and G Meng ldquoA sensor model for defectdetection in mine hoisting wire ropes based on magneticfocusingrdquo InsightmdashNon-Destructive Testing and ConditionMonitoring vol 59 no 3 pp 143ndash148 2017

[7] S Pan D Zhang and E Zhang ldquoNondestructive testing forshallow defect of ferromagnetic objects based on magneticprobe structurerdquo IEEE Transactions on Magnetics vol 54no 11 pp 1ndash6 2018

[8] J-W Kim and S Park ldquoMagnetic flux leakage-based localdamage detection and quantification for steel wire rope non-destructive evaluationrdquo Journal of Intelligent Material Systemsand Structures vol 29 no 17 pp 3396ndash3410 2018

[9] X Yan D Zhang and F Zhao ldquoImprove the signal to noiseratio and installation convenience of the inductive coil forwire rope nondestructive testingrdquo NDT amp E Internationalvol 92 pp 221ndash227 2017

[10] J Li Y Wang X Zhang C Ji and J Shi ldquoSensitivity andresolution enhancement of coupled-core fluxgate magne-tometer by negative feedbackrdquo IEEE Transactions on In-strumentation and Measurement vol 68 no 2 pp 623ndash6312019

[11] H M Lei R H Liang W Tao Y-M Mao and H ZhaoldquoBroken wires inspection for coated steel belts in elevatorsystem using MFL methodrdquo in Proceedings of the 2014 IEEEFar East Forum on Nondestructive EvaluationTestingpp 252ndash254 Chengdu China October 2014

[12] X Liu Y Wang B Wu G Zhen and H Cunfu ldquoDesign oftunnel magnetoresistive-based circular MFL sensor array forthe detection of flaws in steel wire roperdquo Journal of Sensorsvol 2016 Article ID 6198065 8 pages 2016

[13] J Zhang X Tan and P Zheng ldquoNon-destructive detection ofwire rope discontinuities from residual magnetic field imagesusing the Hilbert-Huang transform and compressed sensingrdquoSensors vol 17 no 3 p 608 2017

[14] Y F Wang ldquoResearch on application of wavelet denoisinginto broken wire damage detection of mine steel wire roperdquo inProceedings of the 35th Chinese Control Conference (CCC)pp 6644ndash6648 IEEE Chengdu China August 2016

[15] S Liu Y Sun W Ma et al ldquoA new signal processing methodbased on notch filtering and wavelet denoising in wire ropeinspectionrdquo Journal of Nondestructive Evaluation vol 38no 2 p 39 2019

[16] M Zhao D L Zhang and Z H Zhou ldquo-e research onquantitative inspection technology to wire rope defect basedon hall sensor arrayrdquo Nondestructive Testing vol 34 no 11pp 57ndash60 2012

[17] J Zhang P Zheng and X Tan ldquoRecognition of broken wirerope based on remanence using EEMD and wavelet methodsrdquoSensors vol 18 no 4 p 1110 2018

[18] X Tan and J Zhang ldquoEvaluation of composite wire ropesusing unsaturated magnetic excitation and reconstructionimage with super-resolutionrdquo Applied Sciences vol 8 no 5p 767 2018

[19] L Wie Y Ming J Luan and Y Guo ldquoImage fusion algorithmbased on shift-invariant shearlet transformrdquo Acta PhotonicaSinica vol 42 no 4 pp 496ndash503 2013

[20] H-Y Cai L-R Zhuo P Zhu Z-H Huang and X-Y WuldquoFusion of infrared and visible images based on non-sub-sampled contourlet transform and intuitionistic fuzzy setrdquoActa Photonica Sinica vol 47 no 6 2018

[21] L N Deng and X F Yao ldquoResearch on the fusion algorithmof infrared and visible images based on non-subsampledshearlet transformrdquo Acta Electronica Sinica vol 45 no 12pp 2965ndash2970 2017

[22] G Anbarjafari and H Demirel ldquoImage super resolution basedon interpolation of wavelet domain high frequency subbandsand the spatial domain input imagerdquo ETRI Journal vol 32no 3 pp 390ndash394 2010

[23] G Gao L Xu and D Feng ldquoMulti-focus image fusion basedon non-subsampled shearlet transformrdquo IET Image Process-ing vol 7 no 6 pp 633ndash639 2013

[24] V P S Naidu and J R Raol ldquoPixel-level image fusion usingwavelets and principal component analysisrdquo Defence ScienceJournal vol 58 no 3 pp 338ndash352 2008

[25] P Zhu X Ma and Z Huang ldquoFusion of infrared-visibleimages using improved multi-scale top-hat transform andsuitable fusion rulesrdquo Infrared Physics amp Technology vol 81pp 282ndash295 2017

[26] S Aymaz and C Kose ldquoA novel image decomposition-basedhybrid technique with super-resolution method for multi-focus image fusionrdquo Information Fusion vol 45 pp 113ndash1272019

[27] J Zhou C Zhou J Zhu and D Fan ldquoA method of super-resolution reconstruction for remote sensing image based onnon-subsampled contourlet transformrdquo Acta Optica Sinicavol 35 no 1 article 0110001 2015

[28] Y Freund and R E Schapire ldquoA decision-theoretic gener-alization of on-line learning and an application to boostingrdquoJournal of Computer and System Sciences vol 55 no 1pp 119ndash139 1997


International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

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VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

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Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

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Submit your manuscripts atwwwhindawicom

into multiple coils to realize magnetization which will leadto problems of coil heating and complicated operationPermanent magnet magnetization is easy to operate Andpermanent magnets are usually uniformly arranged aroundthe circumference of the wire rope for magnetization Sunet al [4] proposed an open permanent magnet magneti-zationmethod which solved the problem of strongmagneticforce and large weight in the yoke magnetic method Yu et al[5] considered the effects of excitation system on the per-formance of leakage magnetic nondestructive testing andoptimized the dimensions of the yoke Wang et al [6]combined the structural models for dynamic magnetic fieldbalancing and magnetic focusing which effectively reducedthe interference produced by the interactions between theenvironmental magnetic fields and the wire rope strand Panet al [7] proposed a simple and portable magnetic detectordevice which was very light and easy to design andmanufacture

-e leakage magnetic field on the surface of the wire ropeis usually collected by magnetic sensors which includes Hallsensor [8] coil sensor [9] fluxgate sensor [10] giantmagnetoresistance (GMR) sensor [11] tunnel magnetore-sistance (TMR) sensor [12] -e circumferential resolutionand sensitivity of magnetic sensors array are important forthe detection of wire rope damage -e circumferentialresolution of magnetic sensor array affects the location ofwire rope damage Kim and Park [8] designed a hall sensorarray to detect wire rope damage the MFL signal at thedefect was enveloped and compared with the threshold toquantify the defect However the low circumferential res-olution due to Hall sensor size made it difficult to accuratelylocate the circumferential position of the damage And thesensitivity of the magnetic sensor also affects the effective-ness of detecting damages Yan et al [9] designed a kind ofiron core as coil winding skeleton and their design im-proved the signal-to-noise ratio (SNR) of magnetic fluxleakage signal and simplified coil sensor Lei et al [11]proposed a detection device based on high-sensitivity GMRsensors which solved the problem of low SNR caused by thesmall diameter and large lift off Liu et al [12] designed acircular TMR-based MFL sensor for slight wire rope flawdetection and detected accurately the axial and circumfer-ential positions of these broken wire flaws

After the magnetic sensors obtain the leakage magneticfield on the surface of the wire rope signal processing andimage processing techniques are used to achieve qualitativeand quantitative detection of defects -e MFL signal of thewire rope contains various background noises -e signalprocessing methods are used to filter out these noises whichcan achieve the qualitative analysis of defects Zhang et al[13] designed a wavelet filtering algorithm combining theHilbertndashHuang transformation with compression sensingwhich effectively suppress the system noise Wang [14] usedthe wavelet method to analyze the magnetic flux leakagesignal at the defect and verified the wavelet base db4 with thebest noise reduction effect Liu et al [15] proposed a wirerope detection signal processing method combining notchfilter and wavelet denoising which could effectively dis-tinguish wire rope defect signal and strand signal with high

detection accuracy even for the inner defect Image pro-cessing technology can visualize the magnetic flux leakagesignal of defects and improve the quality and resolution ofdefect images which is important for the quantitativeanalysis of defects Zhao et al [16] used circumferentialinterpolation to improve the circumferential resolution ofthe original MFL images and achieved good detection re-sults Zhang et al [17] proposed wavelet super-resolutiontechnology to improve the resolution of the defect imagemaking the edge information of the defect more obvious andeasier to identify Tan and Zhang [18] proposed a super-resolution reconstruction method based on Tikhonov reg-ularization to enhance defect image and verified that imageproperty was the best when super-resolution result wastriple However this method did not apply the exact imagequality measurement experiments to verify the effect ofdefect image enhancement In addition to these imageprocessing techniques to improve resolution some imagefusion methods can also be used to enhance the MFL imageof wire rope defects In the field of image fusion traditionalmultiscale image representation methods have pyramidwavelet curvelet and shearlet Among them discretewavelet transform is a common method but it has somedisadvantages such as not shift invariance poor di-rectionality and not a time-invariant transform [19]Compared to the wavelet transform contourlet transformhas good multiresolution and directionality but it lacks shiftinvariance Non-subsampled contourlet transform is pro-posed to solve this problem which adds shift invariance tothe contourlet transform and it can better express the edgeand texture information of the image [20] However it is notefficient and takes a long time To solve this problem NSST isproposed It not only has short running time and can meetthe requirements of real-time [21] but also can extract moredetailed features of the target image in different directions

In order to solve the problems of large volume complexoperations and limited circumferential resolution due tosensor size in traditional devices a MFL detection devicebased on the double detection board is designed -e doubledetection board consists of magnetic sensors and thesesensors are staggered and evenly arranged on the circum-ference of the wire rope -e wire rope is magnetized bypermanent magnets and the double detection board collectsthe surface MFL data of the wire rope A super-resolutionalgorithm based on interpolation is applied to double theresolution of defect images NSST is applied to super-res-olution images which are decomposed into high-frequencycoefficients and low-frequency coefficients For high-fre-quency coefficients PCA is implemented as a fusion ruleFor low-frequency coefficients GFL is implemented as afusion rule After fusing these coefficients inverse NSST isapplied to reconstruct the fused image -e proposed al-gorithm fuses the data of double detection board to improvethe quality of defect images Various image quality mea-surement and comparison experiments are performed andthe results show that spatial resolution of defect images isenhanced effectively and high-quality images are obtainedImage descriptions of texture features and invariant mo-ments are extracted as the feature vector of the defect which










3 Data Processing









⎧⎪⎨

⎪⎩(1)









4 Image Processing







Wire ropeNS

(a)


Wire rope

Detection direction

Permanent magnet

(b)


Wire rope

θ

L








First boarddata

Second boarddata

Signal denoise



enhancementExtract


Imageprocessing

Dataprocessing



(a) (b)

(c) (d)

(e) (f )


0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(a)

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(b)















0

500

1000

1500

2000

2500

Am

plitu

de



ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de






p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia










3 Data Processing









⎧⎪⎨

⎪⎩(1)









4 Image Processing







Wire ropeNS

(a)


Wire rope

Detection direction

Permanent magnet

(b)


Wire rope

θ

L








First boarddata

Second boarddata

Signal denoise



enhancementExtract


Imageprocessing

Dataprocessing



(a) (b)

(c) (d)

(e) (f )


0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(a)

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(b)















0

500

1000

1500

2000

2500

Am

plitu

de



ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de






p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




⎧⎪⎨

⎪⎩(1)









4 Image Processing







Wire ropeNS

(a)


Wire rope

Detection direction

Permanent magnet

(b)


Wire rope

θ

L








First boarddata

Second boarddata

Signal denoise



enhancementExtract


Imageprocessing

Dataprocessing



(a) (b)

(c) (d)

(e) (f )


0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(a)

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(b)















0

500

1000

1500

2000

2500

Am

plitu

de



ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de






p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






First boarddata

Second boarddata

Signal denoise



enhancementExtract


Imageprocessing

Dataprocessing



(a) (b)

(c) (d)

(e) (f )


0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(a)

0

10

202 3 410 5

Axial distance

Circ

umfe

rent

ial

chan

nel

500

1000

1500

2000

2500

Am

plitu

de

(b)















0

500

1000

1500

2000

2500

Am

plitu

de



ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de






p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia














0

500

1000

1500

2000

2500

Am

plitu

de



ndash600

ndash400

ndash200

0

200

400

600

800

Am

plitu

de






p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




p1 V1

1113936 V

p2 V2

1113936 V

(3)


fdk(i j) p1f

dAk(i j) + p2f

dBk(i j) (4)





(a) (b)

(c) (d)

(e) (f)


0

12001000

800600400200


05101520


Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(a)


0

12001000

800600400200


0

10

20

Am

plitu

deCi

rcum

fere

ntia

l

chan

nel

(b)





2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




2

2(kσ)21113888 1113889

η1 1 minus η0


(5)







AG 1

m times n

1113936miminus 11113936


2+ 1113936

miminus 21113936


2

2

1113971

(6)



IE minus 1113944L

i0p(i) log2(p(i)) (7)



SD

1m times n

1113944

m

iminus 11113944

n


1m times n

1113944

m

iminus i

1113944

n

jminus 1F(i j)⎡⎢⎢⎣ ⎤⎥⎥⎦

211139741113972

(8)


SF RF2 + CF2

1113968 (9)

RF

1m times n

1113944

m

i21113944

n


2

11139741113972

CF

1m times n

1113944

m

i11113944

n


2

11139741113972

(10)













Fused image








MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






MI MIAF + MIBF (11)

MIXF 1113944xf


pX(x)pF(f) (12)


Table 1 AG


Table 2 IE


Table 3 SD


Table 4 SF







QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




QABF

1113936

Ni11113936

Mj1Q


1113936Ni11113936

Mj1 wA(i j) + wB(i j)( 1113857

(13)




SNRX 20 log101113936

Mi11113936

Nj1 fX(i j)( 1113857

2

1113936Mi11113936


2⎡⎢⎢⎣ ⎤⎥⎥⎦

(15)




u2x + u2

f + C11113874 1113875 σ2x + σ2f + C21113874 1113875

(17)




(a) (b) (c)

(d) (e) (f )










Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia







Training data

Weak classifier 1

Ensemblemodel


Testing data


Weak classifier 3

Weak classifier n



03040506070809

1

Reco

gniti

on ra

te


X 045Y 07097

(a)

040506070809

1

Reco

gniti

on ra

te


X 045Y 08387

(b)

040506070809

1

Reco

gniti

on ra

te


X 045Y 09032

(c)

0405060708

109

Reco

gniti

on ra

te


X 045Y 09355

(d)








7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia







7 Conclusions


Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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