RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

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1 PHOONIC SENSORS / Vol. 8, No. 3, 218: RVFL-Based Optcal Fber Intruson Sgnal Recognton Wth Mult-Level Wavelet Decomposton as Feature Yanpng WANG 1, Danjun GONG 1, Lpng PANG 2*, and Dan YANG 1 1 School of Electronc and Informaton Engneerng, North Chna Unversty of echnology, Bejng 1144, Chna 2 School of Avaton Scence and Engneerng, Bejng Unversty of Aeronautcs and Astronautcs, Bejng 1191, Chna * Correspondng author: Lpng PANG E-mal: panglpng@buaa.edu.cn Abstract: he optcal fber pre-warnng system (OFPS) has been gradually consdered as one of the mportant means for ppelne safety montorng. Intruson sgnal types are correctly dentfed whch could reduce the cost of troubleshootng and mantenance of the ppelne. Most of the prevous feature extracton methods n OFPS are usually quested from the vew of tme doman. However, n some cases, there s no dstngushng feature n the tme doman. In the paper, frstly, the ntruson sgnal features of the runnng, dggng, and pck mattock are extracted n the frequency doman by mult-level wavelet decomposton, that s, the ntruson sgnals are decomposed nto fve bands. Secondly, the average energy rato of dfferent frequency bands s obtaned, whch s consdered as the feature of each ntruson type. Fnally, the feature samples are sent nto the random vector functonal-lnk (RVFL) network for tranng to complete the classfcaton and dentfcaton of the sgnals. Expermental results show that the algorthm can correctly dstngush the dfferent ntruson sgnals and acheve hgher recognton rate. Keywords: OFPS; mult-level wavelet decomposton; optcal fber sgnal recognton; RVFL Ctaton: Yanpng WANG, Danjun GONG, Lpng PANG, and Dan YANG, RVFL-Based Optcal Fber Intruson Sgnal Recognton Wth Mult-Level Wavelet Decomposton as Feature, Photonc Sensors, 218, 8(3): Introducton At present, the ppelne transportaton s one of the man modes of transportaton for ol and natural gas. However, ol or natural gas leakage caused by ppelne damage can lead to serous envronmental polluton and economc loss [1 3]. In response to ths problem, some researches on ppelne detecton have been proposed n domestc and nternatonal [4]. he optcal fber pre-warnng system (OFPS) s often used for dsasters occurrence montorng such as ol and gas ppelne leakage, and t s manly used for detecton and recognton of ntruson source [5, 6]. Some documents proposed they found an effectve way to set the soft or hard thresholds for every pont along the fber adaptvely to mprove the sgnal-to-nose rato (SNR) [7, 8]. Wth dfferent background noses, the constant false alarm rate (CFAR) algorthm s exploted to acheve an optmal adaptve threshold detecton [9, 1]. Above-mentoned methods use the adaptve threshold to mprove the detecton effcency and the system pre-warnng performance, but the false alarm problem s stll more promnent. For example, the vbraton of passng vehcle makes no drect damage to the ppelne, but ths knd of vbraton can stll Receved: 1 March 218 / Revsed: 24 May 218 he Author(s) 218. hs artcle s publshed wth open access at Sprngerlnk.com DOI: 1.17/s Artcle type: Regular

2 Yanpng WANG et al.: RVFL-Based Optcal Fber Intruson Sgnal Recognton Wth Mult-Level Wavelet Decomposton as Feature 235 cause false alarm, whch wll lead to wastng of system resources. One of the key technologes of the OFPS s to accurately dstngush the harmful and harmless ntruson event. Moreover, the dentfcaton of harmful ntruson sgnals s able to ndcate for us the ntruson ponts. herefore, we manly focus on the dentfcaton of the ntruson types based on the dstncton between harmful and harmless sgnals n OFPS n ths paper. Regardng to the feature extracton of the sgnal n OFPS, a feature extracton method for optcal fber sgnals n the tme doman s qute effectve n dstngushng among harmful and harmless ntruson sgnals [11]. However, for dentfyng dfferent types of harmful ntruson events, t performs badly. In our prevous experment, the ntruson sgnals n the tme doman were transformed nto the frequency doman by wavelet transform, and t was found that the frequency spectrum dstrbuton of each harmful ntruson sgnal was qute dfferent. Hence, we analyze the problems n the frequency doman n ths paper, and the ultmate extracted feature n the frequency doman s nput nto random vector functonal-lnk (RVFL) classfer. he nput weghts and bases of RVFL are chosen randomly accordng to the unform dstrbuton n a gven range. he network uses lnear regresson method nstead of multple teratve optmzatons. he above measures greatly reduce the tme consumpton for tranng the networks. he wavelet packet decomposton s a method to recognze the ntruson sgnals, and ts expermental results show that ntruson sgnals, such as gas leakage, artfcal excavaton, and walkng, can be effectvely dstngushed [12]. However, durng the processng, plenty of wavelet coeffcents are generated by the wavelet packet decomposton, whch can nduce the complexty of tranng network, snce t s drectly proportonal to the number of tranng samples. It s known that the mult-level wavelet decomposton has the nature wth fewer wavelet coeffcents than the wavelet packet decomposton [13, 14]. herefore, t s more sutable for the neural networks classfcaton. However, the wavelet coeffcents obtaned by the mult-level wavelet decomposton cannot be drectly nput nto the network as features. So we need to adopt an effectve feature extracton method. he battlefeld-acoustc sgnal s decomposed, and the energy rato s taken as the characterstc vector for classfcaton, where the performance of classfyng the type of battlefeld-acoustc sgnal s obvous [15]. An mproved fast Fourer transform (FF) flterng method based on the energy-rato pretreatment can dstngush the harmful ntruson event and harmless nterference event effcently [16]. In ths paper, the energy rato feature extracton method s utlzed, whch can preserve the frequency band nformaton wth low cost of calculaton. hs energy rato of dfferent frequency bands by mult-layer wavelet decomposton s used as the feature sample, and the effectve nformaton s retaned. At last, the extracted feature data are nput nto the RVFL neural network for tranng and classfcaton. he real feld experment s conducted to verfy the proposed algorthm. he expermental results show that ths algorthm can effectvely recognze the runnng, dggng, and pck mattock sgnals. 2. Algorthm flowchart of feature extracton and classfcaton Fgure 1 shows the processng flow of ths algorthm. Frst of all, we detect three ntruson sgnals and collect data. Furthermore, these sgnals are decomposed by dscrete wavelet transform to obtan the wavelet coeffcents n sx frequency bands, where the average energy rato of wavelet coeffcents n each scale s calculated, respectvely. hen, we reman the average energy rato of the fve bands as an ndependent feature. Fnally, the ntruson sgnal samples of features are nputted nto an RVFL network for tranng and testng.

3 236 Photonc Sensors Detect three ntruson sgnals and collect data Decomposed sgnals by DW Obtan average energy rato of dfferent frequency bands as sample Classfcaton by RVFL NN Runnng, dggng, and pck mattock Feature extracton by multlevel wavelet decomposton Fg. 1 Processng flow of ntruson sgnal recognton. 2.1 Feature extracton from mult-level wavelet decomposton he fundamental strategy for the wavelet transform as a tme-frequency transform method s decomposton-reconstructon. Namely, the orgnal sgnal can be decomposed nto a wavelet seres that can be further used to reconstruct the orgnal one. Moreover, the wavelet seres s actually somewhat features of the orgnal sgnal but more obvous than t. he bass functon of wavelet transform has both scales and dsplacements transformatons. he dscrete wavelet transform (DW) of sgnal s(t) s defned as 1 t (, ) st ( ) ψ dt 2 2 (1) where t s the mother wavelet functon, s the scale factor, and provdes the dsplacement nformaton of mother functon. Hence, the mult-level wavelet decomposton s acheved along wth dfferent scales and dsplacements. he transformaton process s descrbed n Fg. 2. As shown n Fg. 2, LA represents low frequency, and HD represents hgh frequency. hs decomposton s the process of retanng hgh frequency and subdvdng the low frequency. In ths paper, the sgnal s(t) s decomposed nto fve layers by DW, and the low frequency part LA 5 and the hgh frequency parts HD 5 HD 1 are obtaned. hen the average energy n each frequency band s obtaned as N E 2 n N, 1, 2, 3,, 6 (2) n1 where n s the wavelet coeffcent n the th frequency band, and N s the length of the wavelet coeffcent n each frequency band. hen, the total energy can be accumulated by all frequency bands as expressed as follows: E sum 6 E. (3) 1 herefore, the average energy rato n each frequency band s gven as follows: E / E, 1, 2, 3,, 6. (4) sum We fnd that the HD 1 frequency band of three ntruson sgnals have not obvous dfference wth others. Hence, we artfcally remove the HD 1 and retan average energy ratos of remanng fve frequency bands. hen, we take t nto RVFL as ndependent feature of samples, and the fnal feature,,,,. sample s Fg. 2 Structure of fve-layer wavelet decomposton. 2.2 RVFL NN classfcaton he RVFL neural network can overcome the dffculty of slow convergence and local mnmum nherently n the tradtonal gradent-based learnng algorthms. Besdes, the RVFL has drect connectons between the nput layer and the output layer, thus ts weghts no longer need updatng durng learnng as shown n Fg. 3. We calculate the network weghts of nodes connected wth the output layer by solvng smple lnear regresson problems wth the random nput weghts w and bases b. Accordng to the neural network calculaton formula and the unque structure of RVFL, the functon ŷ s gven as

4 Yanpng WANG et al.: RVFL-Based Optcal Fber Intruson Sgnal Recognton Wth Mult-Level Wavelet Decomposton as Feature 237 Output layer Hdden layer Input layer y y 1 k L x1 1 N x xn j, j 1,, N L wj, b j 1 N 1 k L x1 Fg. 3 Structure of the RVFL NN. k 1 L y w xb k k k x xn y y j, j 1,, N L wkx bk (5) where w k s the weght value from the nput layer to hdden layer, k s the weght value from the hdden layer to output layer, wx k bk s the lnear transformaton of the nput vector, and s the bass functon. In ths network, the weght w k and bases b k are randomly set n the learnng process. Hence, the weght k s the only parameter durng learnng resultng n a hgh effcency. In ths paper, we use the mean square error as the objectve functon. So the learnng problem can be formulated as 2 E N L mn f x x k ; w k, b k 1 k1 (6) whch can be rewrtten as E =mn β 2 2 Y Φβ (7) where β s the matrx form of k and Φ can be wrtten as y1 x1; w1, b1 x1; wl, bl y 2 Φ=, Y. xn; w1, b1 xn; wl, bl yn Accordng to the defnton of matrx 2-Norm, (7) becomes as follows: E Y Φβ Y Φβ. (8) mn β akng the dervatve of (8), then we get E =2Φ Φβ Y (9) β where ΦΦ s a full rank or postve-defnte matrx. Let (9) be zero, then β can be obtaned as follows: = 1 β Φ Φ Φ Y. (1) he RVFL network structure can be determned by obtanng the weght β from tranng. hen, the test samples are nput nto the RVFL network to complete classfcaton. 3. Experments on feature extracton and ntruson sgnal recognton 3.1 Collecton of measured data he measured data were collected n Mentougou Dstrct, Bejng, Chna. he obtaned sgnals are runnng, dggng, and pck mattock by usng the CFAR detecton. he resolutons n the tme and spatal dmensons are 1 ms and 1 m. Fgure 4 shows that the abscssa represents the poston, and the ordnate represents the tme. We can see that contnuty ponts are ntruson sgnals when the ntruson locaton s about 2 m and 8 m. me (ms) Poston (m) Fg. 4 Detecton result of optcal vbraton sgnal. 3.2 Feature extracton of ntruson sgnal he orgnal waveforms of runnng, dggng, and pck mattock sgnals n the tme doman are shown n Fg. 5. Accordng to the method of mult-layer

5 238 Photonc Sensors wavelet decomposton, the sgnal frequency spectra are shown n Fg. 6. We realze the 5-level decomposton of runnng, dggng, and pck mattock sgnals by usng db3 wavelet, and the results are shown n Fg. 6, wheren s s the orgnal functon, ca5 and cd1 cd5 are the wavelet coeffcents n dfferent frequency bands. We fnd that the wavelet coeffcents of each layer have a sgnfcant dfference except cd1. herefore, we neglect the cd1 n order to reduce the feature dmenson. And the remanng wavelets coeffcents are used as ndependent features to dstngush the runnng, dggng, and pck mattock sgnals. hen, we calculate ther energy ratos as shown n Fg. 7. Normalzed ampltude Normalzed ampl tude Normalzed ampltude me (ms) (a) me (ms) (b) me (ms) (c) Fg. 5 Orgnal waveforms of vbraton sgnal: (a) runnng sgnals, (b) dggng sgnals, and (c) pck mattock sgnals. s cd1 cd2 cd3 cd4 cd5 ca5 s cd1 cd2 cd3 cd4 cd5 ca5 s cd1 cd2 cd3 cd4 cd5 ca Seral number (bn) (a) Seral number (bn) (b) Seral number (bn) (c) Fg. 6 Result of mult-level wavelet decomposton: (a) runnng sgnals, (b) dggng sgnals, and (c) pck mattock sgnals.

6 Yanpng WANG et al.: RVFL-Based Optcal Fber Intruson Sgnal Recognton Wth Mult-Level Wavelet Decomposton as Feature 239 Energy (bn) ca5 cb5 cd4 cd3 cd2 Wavelet coeffcents (bn) Runnng Dggng Pck mattock Fg. 7 Energy rato of wavelet decomposton coeffcent for ntruson sgnals. It can be seen from Fg. 7 that the energy rato has sgnfcant dfferences regardng to three knds of ntruson sgnals. he energy of runnng sgnal s concentrated n the low frequency part ca5, whle the energy of dggng sgnal n the ntermedate frequency cd4 accounts for the hghest one. Moreover, the energy of pck mattock sgnal s concentrated n the hgh frequency part cd2. So, we use the energy rato as samples for the ntruson sgnal classfcaton by RVFL NN. 3.3 Identfcaton of ntruson sgnal by RVFL NN In the experments, we have totally three knds of ntruson samples ncludng runnng, dggng, and pck mattock, whch are traned n pars labeled wth dgtal numbers and 1, respectvely. For example, we set the runnng sgnal samples wth label 1 whle both the dggng sgnal and the pck mattock sgnal are set wth label together. ranng samples are used to tran to fnd a proper weght β. he test samples are nput nto the RVFL NN model obtaned by tranng and the test results are shown n Fg. 8. It can be clearly seen that the recognton of the runnng sgnals s completed after tranng. We repeat the above processng flow on the other two types of ntruson sgnals. Fnally, all the three types of ntruson sgnals are classfed successfully. In Fg. 8, the recognton threshold s set to be and the samples wth recognton results larger than wll be regarded as the runnng sgnals. Otherwse, the samples wll be regarded as the dggng and pck mattock sgnals. Smlarly, the same recognton strategy s performed on the dggng sgnal as shown n Fg. 8(b). Fnally, we have fnshed the classfcaton for the ntruson sgnals. o make further accuracy analyss, we calculate the RVFL NN recognton rate of the test sample as shown n able 1. Recognton results Recognton results Samples sequence (bn) (a) Samples sequence (bn) (b) Fg. 8 Recognton result of RVFL NN: (a) classfcaton results of runnng sgnals and (b) classfcaton results of dggng sgnals. able 1 Recognton rate of RVFL NN. Results Real sgnals Runnng sgnal Dggng sgnal Pck mattock sgnal Recognton accuracy (%) Runnng sgnal Dggng sgnal Pck mattock sgnal It can be seen from the able 1 that the method provded n ths paper can effectvely dentfes the runnng sgnal, the dggng sgnal, and the pck mattock sgnal n the optcal fber n the OFPS and acheves hgh recognton rate whch s 92%, 94%, and 96%, respectvely. 4. Conclusons In ths paper, the recognton of vbraton sgnal s studed. Frstly, we extract ntruson sgnals n the

7 24 Photonc Sensors frequency doman by the fve-level wavelet decomposton. hen, accordng to the characterstcs of dfferent vbraton sgnals, the energy rato feature extracton method s drawn on. After that, we calculate the average energy rato as sample nto the RVFL NN to complete the dentfcaton and classfcaton. he recognton rates of the runnng sgnal, the dggng sgnal, and the pck mattock sgnal are 92%, 94%, and 96%, respectvely. he feld experment results show that we can correctly dentfy the ntruson sgnals, whch verfes the feasblty and effectveness of the proposed algorthm. Acknowledgment he authors wsh to express ther grattude to the anonymous revewers and the assocate edtor for ther rgorous comments durng the revew process. In addton, authors also would lke to thank SUN Chengbn and AN Le n our laboratory for ther great contrbutons to the data-collecton work. hs work was supported by the Natonal Natural Scence Foundaton of Chna (Grant Nos and 61616), Bejng Nature Scence Foundaton (Grant No ), and Bejng Muncpal Scence and echnology Project (Grant No. Z ). Open Access hs artcle s dstrbuted under the terms of the Creatve Commons Attrbuton 4. Internatonal Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded you gve approprate credt to the orgnal author(s) and the source, provde a lnk to the Creatve Commons lcense, and ndcate f changes were made. References [1] W. Q. Lu, W. Lang, L. B. Zhang, and W. Lu, A novel nose reducton method appled n negatve pressure wave for ppelne leakage localzaton, Process Safety and Envronmental Protecton, 216, 14(part A): [2] K. Jng and Z. H. Zou, me predcton model for ppelne leakage based on grey relatonal analyss, Physcs Proceda, 212, 25(2): [3] W. Lang, L. L. Lu, and L. B. Zhang, Couplng relatons and early-warnng for equpment chan n long-dstance ppelne, Mechancal Systems and Sgnal Processng, 213, 41(1 2): [4] X. Wang and M. S. Ghdaou, Identfcaton of multple leaks n ppelne: lnearzed model, maxmum lkelhood, and super-resoluton localzaton, Mechancal Systems and Sgnal Processng, 218, 17: [5] M. Zadkaram, M. Shahbazan, and K. Salahshoor, Ppelne leakage detecton and solaton: an ntegrated approach of statstcal and wavelet feature extracton wth mult-layer perceptron neural network (MLPNN), Journal of Loss Preventon n the Process Industres, 216, 43: [6] G. Allwood, G. Wld, and S. Hnckley, Optcal fber sensors n physcal ntruson detecton systems: a revew, IEEE Sensors Journal, 216, 16(14): [7] Y. Zhan, Q. Yu, K. Wang, F. Yang, Y. Kong, and X. Zhao, A hgh performance dstrbuted sensor system wth mult-ntrusons smultaneous detecton capablty based on phase senstve ODR, Opto-Electroncs Revew, 215, 23(3): [8] Y. Sh, H. Feng, Y. An, X. Geng, and Z. M. Zeng, Research on wavelet analyss for ppelne pre-warnng system based on phase-senstve optcal tme doman reflectometry, n Proceedng of IEEE/ASME Internatonal Conference on Advanced Intellgent Mechatroncs (AIM), Besancon, France, 214, pp [9] H. Q. Qu,. Zheng, F. K. B, and L. P. Pang, A vbraton detecton method for optcal fber pre-warnng system, IE Sgnal Processng, 216, 1(6): [1] Z. Z. Qu,. Zheng, H. Q. Qu, and L. P. Pang, A new detecton method based on CFAR and DE for OFPS, Photonc Sensors, 216, 6(3): [11] N. Lu, B. W. An, and Y. L. L, Sgnal recognton algorthm of fber-optc securty system based on tme-doman features, ransducer and Mcrosystem echnologes, 217, 36(4): [12] L. Zhu, Z. M. Zeng, J. C. Zhang, and S. J. Jn, Feature extracton of vbraton sgnal detected by optcal fber along crude ol ppelne and forewarnng system based on ICA, n Proceedng of Internatonal Workshop on Intellgent Systems and Applcatons, Wuhan, Chna, 29, pp [13] H. J. Wu, Z. N. Wang, F. Peng, Z. P. Peng, X. Y. L, Y. Wu, et al., Feld test of a fully dstrbuted fber optc ntruson detecton system for long-dstance

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