Approach to Identifying Raindrop Vibration Signal Detected by Optical Fiber

Transcription

1 Sensors & Transducers, o. 6, Issue, December 3, pp Sensors & Transducers 3 by IFSA Approach to Identifying Raindrop ibration Signa Detected by Optica Fiber ongquan QU, Shouwen LIU, Yongiao ANG, Guoxiang LI Coege of Information Engineering, North China University of Technoogy, Beiing, 4 China Te.: , fax: E-mai: qhqphd@63.com Received: 3 September 3 /Accepted: November 3 /Pubished: 3 December 3 Abstract: Optica Fiber ibration pre-arning System (OFS) is widey appied to pipeine transportation, defense boundary and miitary base. One of its ey technoogies is signa feature extraction and vibration source identification. owever, some harmess vibration signas often affect the reiabiity of this identification process due to the fase aarms. Therefore, it is very important to identify various harmess vibration signas effectivey. In this paper, we anayze the energy distribution feature of nature raindrop vibration signa detected by optica fiber. Based on this anaysis, we deveop an energy information entropy mode and an approach to identify the harmess raindrop vibration signa. Study shows that the nature raindrop vibration signa can be detected and identified automaticay by extracting the energy information entropy vaue and combining with the statistica detection method. The fied tests resut aso showed that this approach based on energy information entropy mode is abe to effectivey identify harmess raindrop vibration signa. Its identification probabiity is high and its fase aarm and fase recognition probabiity is ow, hence the woring performance of the OFS can be improved by using the presented approach. Copyright 3 IFSA. Keywords: Fiber vibration sensors, Information Entropy, Feature extraction, ibration signa, ariation coefficient.. Introduction ith the rapid deveopment of pipeine transportation, some constructions often happen aong the pipeine and wi threaten the security of pipeine seriousy. It needs some active means of defense to ensure the safety of pipe. The OFS uses the optica fiber cabe aid aong the pipeine as distribution sensors to detecting various vibration signa [-3]. Fig. shows a schematic diagram of the OFS. The optica cabe ain aong the pipeine can obtain vibration signa by using distributed vibration signa sensor [4-7]. hen a vibration source appears, it produces mechanica vibration signa, which is transmitted by the optica fiber to a photodetector sensor, and then to the computer after the photoeectric conversion and anaog-to-digita conversion. The computer anayzes these vibration signas. In this way, the OFS can reaize reatime monitoring and aarming. Samping frequency of photodetector is 5 z. A 8-order FIR bandpass fiter is often used to fiter and process the sampe data. It can overcome signa interference produced by other ow-frequency and high-frequency signa during signa transmission process. Its passband range is z to 3 z. In the pipeine running, there are various vibration signas. Some of these are harmfu and they might be produced by eectrica dris, broen road machines, human destroying optic fiber cabe, arge-scae Artice number P_583 85

2 Sensors & Transducers, o. 6, Issue, December 3, pp construction machines, and so on. Some are harmess and might be produced by human nocing the we covers, rain dropping into the we, train, car, and so on. These harmess vibration sources wi not threaten the safety of the pipeine, but their vibration signas produced by these sources wi interfere with the identification performance for the harmfu source because they wi cause bady fase aarm. Therefore, it is very important to accuratey identify these harmess vibration sources. It is one of the eys for the OFS [8]. the China Petroeum Pipeine Corporation by using the spectrum anaysis method. Fig. shows the spectrum anaysis resuts of vibration signas generated by raindrop, broen road machine, argescae construction machine, human nocing we cover or optica fiber cabe. Ampitude raindrop Frequncy/z. broen road machine.5 Fig.. Schematic diagram of OFS. The current methods to identifying the vibration signa of optica fiber incude inear cassifiers, neura networ methods, anaysis of variance, chaos anaysis, SM-based recognition method [9]. But these methods have in a common feature, that is, they need a arge number of sampes for earning and training, hence the computation oad is heavy, and there is sti convergence issue in its practica appication. In this paper, we present an approach to identify raindrop vibration signa detected by optica fiber based on an energy information entropy mode and a variation coefficient. This approach can extract the main signa features of raindrop (uniform signa energy and stabe energy probabiity), hence it can identify a raindrop vibration signa effectivey. In addition, it does not need a training process, hence its computation oad is very sma.. Pre-anaysis of Typica ibration Signas.. Spectrum Anaysis In an actua pipe running, vibration signa is often generated by some typica sources, such as raindrop, eectrica dri, broen road machine, human nocing a we cover or optic fiber cabe, arge-scae construction machine, train and so on. The conventiona method to processing the vibration signas is to use the spectrum anaysis, which transfers compex vibration signas from the time domain into the spectrum domain, so as to extract some expectant features for source identification. In this section, we anayze some vibration signas monitored by the OFS from some oi pipeines of Ampitude Ampitude Ampitude Ampitude Frequncy/z construction machine Frequncy/z nocing optica fiber Frequncy/z nocing we cover Frequncy/z Fig.. Spectrum anaysis resuts of typica vibration signas. 86

3 Sensors & Transducers, o. 6, Issue, December 3, pp From Fig. we can observe that: their frequency band is simiar in spectrum domain, hence it is difficut to distinguish between the harmess raindrop vibration signa and the other harm vibration signas. This wi cause fase aarm and reduce the woring reiabiity of the OFS. Therefore, it is impossibe to identify a raindrop signa by using the spectrum anaysis. It needs to deveop the other method to extract the features of raindrop signa... Signa Energy Anaysis In this section, we anayze the features of the above vibration signas by using signa energy anaysis method. Based on this, we wi deveop an approach to identify harmess raindrop vibration source. The signa energy per (4 samping data in 4 ms) can be cacuated with Eq. (): w 4 og xi i, () where w is the signa energy in the th, db; x i is the ampitude of i th detected vibration signa. Fig. 3 shows signa energy scatter pots produced by a raindrop, a broen road machine, a arge-scae construction machine, human nocing we cover and optica fiber cabe, respectivey. In this Fig., the abscissa is the number of time sequence, and the ordinate is the signa energy per, w. The signa energy is divided into intervas from - db to db, and then the frequency of signa energy per can be statisticay cacuated in per interva. In this way, we can obtain the discrete probabiity density function of signa energy, as shown in Fig. 4. From Fig. 3~4, we can observe that: ) The vibration signa energy of nature raindrop has three typica features, that is, uniform signa energy, stabe energy probabiity distribution, and vibration existing stabe for a ong time. ) The other signas did not have these three features in the same time. It is easy to extract the third feature (vibration existing stabe for a ong time), hence we ust focus on deveoping a statistica mode to extract the other two features of raindrop vibration signa. - raindrop x 4 - broen road machine construction machine nocing optica fiber Energy Information Entropy Mode In this section, in order to identify the harmess raindrop vibration source, we wi deveop an energy information entropy mode and use this mode to extract the two vibration signa features, uniform signa energy and stabe energy probabiity distribution. - nocing we cover Fig. 3. Signa energy scatter pots. 87

4 Sensors & Transducers, o. 6, Issue, December 3, pp p raindrop broen road machine construction machine nocing optica fiber nocing we cover - - Fig. 4. Discrete probabiity density function of signa energy. 3.. Information Entropy In information theory, entropy is used to measure an expectation of a random variabe appearing. It represents the amount of oss information in the transmission process before it is received. It is aso nown as information entropy or Shannon entropy. The information entropy of a random variabe, x,,, xm xm, can be defined in Eq. (): m X p x p x () og m where p is the probabiity density function of x, x,, m xm ; m is the number of the first dimension. For a two dimensiona case ( m n ), the information entropy of x,, xmn,,, xm, n can be cacuated with Eq. (3) [6]: n m ( X) [ p og ( p )] n m m (3) m mn, mn, where n is the number of the second dimension. The magnitude of information entropy is cosey reated to its probabiity density function. If its probabiity density is uniform distribution, then its information entropy wi reach the imum vaue. 3.. Energy Information Entropy Mode for ibration Signa Base on the concept of information entropy, energy information entropy mode for vibration signa detected by the OFS can be deveoped further, as shown in Eq. (4). n m [ m, nog ( m, n)] (4) n m p p where is the energy information entropy of vibration signa in the th minute; p mn, is the probabiity density function of w. In order to obtain energy information entropy of vibration signa per minute,, we wi cacuate the probabiity density function of w, p mn, : According to the distribution of the signa energy, we set intervas on the energy axis, which is divided into m intervas from - db to db, here et m =. On the axis, it is divided into n intervas with a fixed step, here et n =6. In this way, a two-dimensiona space, energy-, can be divided into m n grids, as shown in Fig. 5. e can account the number of energy data in every grid, and obtain the probabiity, p mn,, in every grid. If there is no energy data in a grid, then it represents this grid interva has no contribution to the cacuation of energy information entropy, hence pmn, og ( pmn, ) Fig. 5. Divided energy- space for one minute. After obtaining the probabiity distribution, the energy information entropy per minute,, can be cacuated with Eq. (4). The imum entropy vaue wi appear when a probabiities in a grids are same: n m, m, n m, n n m [ p og ( p )] og ( m n ) (5) In order to compare the resuts, we wi normaize the energy information entropy per minute by divided by :, (6), 3.3. Stabiity of Energy Information Entropy e can adopt two methods to assess the stabiity of energy information entropy. ) Average distance of 88

5 Sensors & Transducers, o. 6, Issue, December 3, pp e can use a conception of average distance, D, to refect the stabiity of energy information entropy. D is defined as foows: D ref (7) where D is the average distance of {,..,,.., } to a reference vaue, ref in ref time interva [, ] ; is the reference vaue and is a statistica average vaue of a prior chosen segment of raindrop vibration signas. It is important to choose a reasonabe vaue of ref. By anayzing our prior nown raindrop ref vibration signas, we et =.94. ) ariation coefficient of e can aso adopt a variation coefficient to refect the stabiity of energy information entropy. The variation coefficient (C ) is a normaized measure of dispersion of a probabiity distribution. The absoute vaue of C is sometimes nown as reative standard deviation, which is expressed as a percentage. C is defined as the ratio of the standard deviation to the mean: C (8) where C is the variation coefficient of {,..,,.., } in time interva [, +Δ]; is ;. the mean, deviation, is the standard The smaer C is, the smaer the variation degree of {,..,,.., } wi be, and the more stabe this variabe wi be. 4. Approach to Identifying Raindrop ibration Source 4.. Identification Process The normaized energy information entropy of vibration signa ( ), the average distance (D ) and the variation coefficient ( C ) are abe to refect the two features, the uniform signa energy and the stabe energy probabiity distribution. e can use Eq. (4), (7) and (8) to extract these two features of raindrop vibration signa detected by optica fiber. e wi deveop an identification approach to obtain these parameters. This approach wi be expained with an exampe, in which the identification time is 6 minutes, as shown in Fig. 6. The approach is reaized as foowing: ) Every 4 s, the OFS sampes a vibration signa, x. i ) In one (4ms), there wi be 4 signa vibration data. A signa data set, { x,..., xi,..., x4}, can be formed in every 4 ms. Correspondingy, we can cacuate the signa energy, w, in this with Eq. (). 3) In one minute, there wi be 5 s, hence there wi be 5 signa data sets and their corresponding signa energy set, { w,..., w,..., w5}. e divide the -dimentiona space of energy by etting w db and 5. The energy- space is divided into 6 grids (m = and n =6), as shown in Fig. 5. The probabiity distribution of energy per minute, { p,,..., pm, n,..., p, 6}, can be obtained. ence we can cacuate the energy information entropy of vibration signa per minute,, with Eq. (4) and its normaized vaue,. 4) From to minutes (et Δ=min), there wi produce energy information entropies of vibration signa, {,...,,..., }, then we ref cacuate one average distance, D, by using. e can aso cacuate one variation coefficient, C. In this way, we obtain the other D and C in every minute interva. 5) At ast, a normaized energy information 6 entropy set, {,...,,..., }, an average distance set, D,..., D,..., } and a variation coefficient set, { C { D7 7,..., C,..., C }, are formed. e set two threshods, D and C, to udge if the vibration signa detected by the OFS is produced by a raindrop source or not. The criterion is as shown in Eq. (8): D C D C (9) If the vibration signa satisfies Eq. (9), then it is produced by a harmess raindrop source and it does not need aarm; otherwise, it is produced by other vibration source. 89

6 Sensors & Transducers, o. 6, Issue, December 3, pp x { x,.., x,.., x } w i 4 4 og xi i { w,.., w,.., w } 5 { p,.., p,.., p }, mn,,6 6 m, n m, n n m p og ( p ) {,..,,.., } D C {,..,,.., } D C 7 6 {,..,,.., } D 7 7 C 6 {,..,,.., } { D,.., D,.., D } 7 7 { C,.., C,.., C} Fig. 6. Process to extracting, D and C. 4.. ibration Source Identification Resut e sti use those vibration signas monitored by the OFS from some oi pipeines of the China Petroeum Pipeine Corporation for extracting their, D and C. The resuts are as shown in Figs e use a ogarithmic y-axis in Fig. 9 in order to show the difference ceary. Fig. 7. Extracting resut of. 9

7 Sensors & Transducers, o. 6, Issue, December 3, pp Fig. 8. Extracting resut of D. harmess raindrop vibration signa can be identified from the other detected signas finay. In order to investigate the performance of the presented approach, fied data were used. The anaysis resuts show that: the obvious features of raindrop signa can be extracted very we by cacuating, D and C, hence the raindrop vibration source can be distinguished from the other vibration sources evidenty. This presented approach based on energy information entropy can identify the harmess raindrop vibration source accuratey and hep to improve its woring reiabiity by reducing this probabiity of fase aarm. References Fig. 9. Extracting resut of C. Let D =. and C =.3. From Figs. 7-9, we can observe that: the priori nown raindrop vibration signa satisfies criterion very we, hence the harmess raindrop vibration source can be identified and distinguished from other vibration sources evidenty. In addition, Fig. 8 shows the feature of the stabiity of energy information entropy for raindrop vibration signas very we. Therefore, this identification approach based on the energy information entropy and its variation coefficient can hep to improve the woring reiabiity of the OFMS by reducing the probabiity of fase aarm. 5. Concusions In order to improve the woring reiabiity of the OFS, the ey way is to deveop an effective approach to identifying harmess vibration signas and to reduce the probabiity of fase aarm. In this paper, an approach based on energy information entropy was presented to identify raindrop vibration signa detected by the OFS. This approach is abe to extract two obvious features of raindrop vibration signa, uniform signa energy and stabe energy probabiity distribution, by using the normaized energy information entropy of vibration signa ( ), the average distance ( D ) and the variation coefficient ( C ). Combined with their threshods, the []. Y. R. Garcia, J. M. Corres, J. Goicoechea, ibration detection using optica fiber sensors, Journa of Sensors, Artice ID ,, pages. []. G. Murtaza, S. L. Jones, J. M. Senior, N. aigh, Loss behavior of singe-mode optica fiber microbend sensors, Fiber and Integrated Optics, o., Issue,, pp [3]. N. K. Pandey, B. C. Yadav, Embedded fibre optic microbend sensor for measurement of high pressure and crac detection, Sensors and Actuators A, o. 8, Issue, 6, pp [4].. Feng, Y. An, S. J. Jin, Z. M. Zeng, Desensitization Eimination in Pipeine Leaage Detection and Prewarning System Based on the Modeing using Jones Matrix, in Proceedings of the IEEE Sensors Appications Symposium (SAS),, pp. -4. [5]. S. Binu,. P. M. Piai, N. Chandrasearan, Fibre optic dispacement sensor for the measurement of ampitude and frequency of vibration, Optics and Laser Technoogy, o. 39, Issue 8, 7, pp [6]. G. id, S. incey, Acousto-utrasonic optica fiber sensors: overview and state-of-the-art, IEEE Sensors Journa, o. 8, Issue 7, 8, pp [7]. B. Cushaw, Optica fiber sensor technoogies: opportunities and-perhaps-pitfas, Journa of Lightwave Technoogy, o., Issue, 4, pp. 39. [8]. Y. Zhou, S. J. Jin, Y. C. Zhang, Distributed optica fiber sensing technoogy for pipeine eaage detection and ocation, Acta Petroei Sinica, o. 7, Issue, 6, pp. -4. [9]. S. Tarr, G. Leach, The dependence of detection system performance on fence construction and detector ocation, in Proceedings of the Internationa Carnahan Conference on Security Technoogy, Aexandria, A, USA, -4 Oct, 998. []. S. S. Mahmoud, J. Katsifois, Performance Investigation of Rea-time Fiber Optic Perimeter Intrusion Detection Systems Using Event Cassification. Security Technoogy (ICCST), in Proceedings of IEEE Internationa Carnahan Conference on Security Technoogy, San Jose, Caifornia, USA, 5-8 October,. []. J. ries, A ow cost fence impact cassification system with neura networs, in Proceedings of IEEE Africon, o., 4, pp

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