An Acoustic Method for Condition Classification in Live Sewer Networks

Transcription

1 18th World Coferece o Nodestructve Testg, 16-2 Aprl 212, Durba, South Afrca A Acoustc Method for Codto Classfcato Lve Sewer Networks Zao FENG, Krll V. HOROSHENKOV, M. Tareq BIN ALI, Smo J. TAIT School of Egeerg, Desg ad Techology, Uversty of Bradford, Bradford, BD7 1DP, UK Phoe: Z.feg2@bradford.ac.uk K.Horoshekov@bradford.ac.uk m.t.bal@bradford.ac.uk s.tat@bradford.ac.uk Abstract Udergroud ppes are a mportat part of urba water frastructure. These ppes are gradually deteroratg due to agg, operatoal stresses ad evrometal codtos. I order to be able to maage the udergroud ppe system effcetly, codto motorg s eeded to provde a clear uderstadg of the behavor of sewer systems uder varous hydraulc codtos. Ths paper reports o the applcato of a ovel acoustc method to study the evoluto of blockages ad varous types of damage a full scale lfe sewer ppe whch has bee stalled the hydraulc laboratory at the Uversty of Bradford. Temporal ad frequecy characterstcs the behavor of the acoustc testy are extracted from the acoustc sgals recorded o a array of mcrophoes. These characterstcs are used for patter recogto whch s based o K-earest eghbors (KNN) classfer. The obtaed results dcate that the patter recogto system ca provde a relable classfcato of the ppe codto the presece ad absece of flow. Keywords: Udergroud ppe, acoustc testy, patter recogto, codto classfcato I. Itroducto Iteral specto of ppeles s doe by detecto systems ragg from smple vsual specto to complex magg systems. Ulke covetoal CCTV system ad may other alteratves, acoustc-based methods for specto of sewers to recogze ppe codtos ca be fast, o-vasve ad performed o those lfe ppes whch are mpassable for a CCTV robot. A laboratory expermetal set-up to study the evoluto of blockages ad effect of damage o the acoustc sgal propagato has bee stalled Hydraulc Laboratory at the Uversty of Bradford. The results preseted ths paper are based o the aalyss of acoustc sgals whch are reflected from varous objects deposted the partly flled ppe. It s show that these sgals carry suffcet formato about the codtos of ppe, amout of deposted sedmets ad presece of lateral coectos. Soud testy data are used to extract meagful features for classfcato purpose. Soud pressure has bee used tradtoally to aalyze the codtos ppes. Ths paper s based o the aalyss of soud testy whch s, ulke soud pressure, s vector whch drecto cocdes wth the drecto whch the acoustc eergy propagates.

2 Features extracted from the testy data from a sgature database for a rage of dfferet ppe codtos whch are the used as trag ad testg data classfcato procedures. The classfcato algorthm appled ths work s K-earest eghbors (KNN) method. The KNN s a dstace-based classfer whch s easy to perform. The method does t requre ay kowledge about the system of posteror probabltes. The acoustc testy data whch are used ths work are fltered several frequecy bads so that temporal ad frequecy features of the reflected acoustc eergy could be used as ma the features the KNN trag ad subsequet recogto process. Ths paper s orgazed as follows: Secto II presets the expermetal set-up ad data collecto, ad sgal pre-processg methods. Secto III presets a bref troducto of the classfcato methodology, feature extracto ad classfcato results. Secto IV presets the dscusso of the accuracy ad stablty of ths method.. II. Expermetal Testg A 15mm dameter, 14.4meter log clay ppe was costructed the Hydraulcs Laboratory at the Uversty of Bradford. Ths type of ppe s represetatve of small ad medum ppes typcally foud the UK s udergroud sewer etwork. A lateral coected was stalled the mddle of the ppe through whch smulated blockage ca be mplated. The ed of the ppe was coected to a water tak whch was capable of dschargg water at a chage of flow rates. The ppe was set o a sold steel beam of the same legth. Ths expermetal setup s show Fgure 1 (a) ad (b). A acoustc sesor whch was used these expermets cossted of four -le MEMS mcrophoes arraged a PCB board. It was attached to a small loudspeaker whch was able to reproduce soud the audo bad. The spacg betwee the mcrophoes was less tha the acoustc wavelegth to allow for the testy measuremets. The sesor was coected to a soud card whch stalled PC (see Fgure 1 (c)). The sesor was attached to the ppe wall of oe ed of the clay ppe ad aother ed was ether ope or blocked. Broadbad sgals of 3 secods were geerated va loudspeaker ad ts reflectos were recorded o four mcrophoes to obta acoustc testy. Three sets of expermets were carred out wth water level whch was vared from to 2mm sde the ppe to smulate the dry flow codtos typcal for real udergroud lfe sewers. Acoustc sgals were collected for the followg codtos: (1) empty ppe wth closed lateral coecto; (2) empty ppe wth ope lateral coecto; (3) ppe wth a blockage ad closed lateral coecto.

3 Soud Pressure [Pa] Soud Pressure [Pa] (a) (b) (c) Fgure 1.The 15mm clay ppe faclty (a); the lateral coecto (b) ad sesor postoed at the dowstream ed of the ppe (c). A 1 secod susodal sweep the frequecy rage of 5Hz to 15Hz was used as put sgal. Ths type of tme-varat sgal s wdely used to measure the trasfer fucto ad t s well suted for outdoor measuremets. It s less vulerable to the deleterous effect of tme varace [1] ad presece of backgroud ose. Recorded reflecto sgal was decovolved to obta the acoustc pressure mpulse respose whch cotaed formato o ppe geometry, soud speed ad operatoal codtos. The broadbad mpulse respose fltered several arrow bads sgal usg a dgtal Butterworth flter. Fgure 2 (left) shows a example of the orgal susodal sweep sgal recorded o the four mcrophoes the clea 15mm ppe. Fgure 2 (rght) shows the correspodg mpulse respose fltered the frequecy rage of 1-1Hz Ch1 Ch2 Ch3 Ch Dstace (m) Dstace (m) Fgure 2 Recorded pressure sgal (left) ad ts fltered mpulse respose (rght) of clea ppe Ch1 Ch2 Ch3 Ch4

4 Itesty [Pa/m2] Itesty [Pa/m2] The acoustc testy I(t) ca be calculated from data recorded o oe par of mcrophoes usg equato (1) ad (2), where p(t) s the acoustc pressure, u(t) s the acoustc (partcle) velocty the drecto of the ormal that cocdes wth the drecto of soud wave propagato, Here s the desty of ar. However, t s dffcult to extract exact value of p ad p at the same posto, therefore, the approxmato (3) ad (4) are commoly used, where p () t ad p () t are 1 2 the soud pressures measured o the two mcrophoes whch are spaced at the dstace s the acoustc wavelegth [1]., Fgure 3 preset the acoustc testy the clea ppe ad the clea ppe wth a ope lateral coecto calculated accordg to ths method the frequecy rage of 3 45 Hz. A strog reflecto at approxmately 15m ad a smaller reflecto at 8m (rght) ca be see clearly the testy plots preseted as a fucto of dstace. ~ I( t) p( t) u( t) (1) 1 p u() t d (2) pt () p ( t) p ( t) (3) t 1 u( t) p ( ) p ( ) d 1 2 (4).5 x x Dstace (m) Dstace (m) Fgure 3 The testy respose of clea 15mm ppe (left) ad the ppe wth a ope lateral coecto (rght) calculated the 3-45 Hz rage

5 III. Classfcato ad Results Each patter s rule descrbg relato betwee certa cotext, problem ad soluto. s the defto of patter from Chrstopher Alexader [2]. Patter s ot cosdered a soluto but a descrpto ad geeralzato of the experece whch leads to the method how to solve the problem. Patter classfcato s the orgazato of patters to groups of those sharg the same set of propertes. A typcal classfcato system ormally cotas four steps: data processg, feature extracto, feature selecto ad patter classfcato [3]. The K-earest eghbors (KNN) method has proved to be a smple but effectve o-parametrc classfcato algorthm. It s based o the use of dstace measuremet. Gve trag data R ( x, y ),...,( x, y ) 1 1 as a set of labeled samples, KNN classfer assgs a test sample T( x, y ) to the label assocates wth ts K umber of closest eghbors R. The Eucldea dstace s ormally used to calculate the dstace betwee test sample ad trag samples,.e. d R T j1 j 2. The classfcato s doe by a majorty-votg rule, whch states that the label assged to the test sample should be the oe whch occurs the most amog the K earest eghbors. Ppe ed, blockage ad lateral coecto were 3 codtos for whch the reflected acoustc eergy was extracted ad used as the sgatures the classfcato process. For each ppe codto, 2 expermets were carred out wth varable water level. A half of these sgatures were used to tra for the KNN classfer ad the other half were used for testg. The acoustc eergy was calculated from the testy sgals fltered the 2 frequecy bads. It was used as the ma feature the classfcato process. Fgure 4 shows examples of sgatures of recorded from the ppe ed ad lateral coecto. From the testy plots t ca be see clearly that there are recogzable dfferece the soud testy patters whch ca be used for the sgature classfcato. These testy data were used to calculate the acoustc eergy was obtaed for each frequecy bad accordg to the followg equato: t 2 2 E I () t dt t1 (5)

6 Eergy Itesty [Pa/m2] Itesty [Pa/m2] where [ t 1 t 2 ] s the tme wdow chose for ths tegrato process. Each sgature the sgature database was bascally a testg data matrx whch cotaed 1 rows correspodg to dfferet water levels ad 2 colums correspodg to dfferet frequecy bads. Therefore, each elemet of ths matrx was the eergy value oe specfc frequecy bad ad for oe partcular water level Hz 3-45 Hz 45-6 Hz 6-75 Hz Hz 3-45 Hz 45-6 Hz 6-75 Hz Dstace (m) Dstace (m) Fgure 4 Sgature testy plot of ppe ed (left) ad lateral coecto (rght) 3 25 Ppe Ed of empty ppe Ppe Ed of ope Lateral Lateral Coecto Blockage Frequecy Bad (1-1Hz) Fgure 5. A example of the sgature plot

7 Fgure 5 presets the frequecy depedece of the eergy extracted for the followg ppe codtos: ppe ed of the clea ppe; ppe ed of the clea ppe wth a ope lateral coecto; lateral coecto ad the blockage. The fgure llustrates uque patters each of the four sgatures ad shows that these patters ca be dscrmated clearly wth the frequecy rage of 15-45Hz. The ma advatage of the KNN algorthm s that t leads to a very smple approxmato of the Bayesa classfer, called the majorty votg rule. Assume a trag data set X X X 1, 2 N cotag total N samples, amog whch samples are labeled, X class (1 N), s the umber of classes. Here comes a ukow sample x whch eeds to be classfed. Draw a hyper-sphere of volume V aroud x as the estmato rage, amog whch m samples are labeled, X m (1 m V ). Pck K umber of samples whch are the earest eghbors of x from K The lkelhood fucto of desty estmato usg the KNN s: Px ( ), smlarly, the V m ucodtoal desty s estmated by: Px ( ), ad the prors are approxmated by: NV P( ). Therefore, the Bayesa classfer [4] becomes: N K P( x ) P( ) K P( x) V N (6) P( x) m m NV X m A large value of K yelds smoother decso regos, a smaller value of K mproves the classfcato effcecy. Normally choose K N for N umber of samples usg the rule of thumb. It s expected that K should be a odd teger to avod tes. I ths paper, N=6 at oe frequecy bad, hece K=7 was chose. The frequecy rage of 1 45 Hz was foud to be the useful rage to determe sgature types (see Fgure 5). Ths frequecy rage was splt 5 frequecy bads whch were used the classfcato process. The sgature types PE, BK ad LC stad for ppe ed, blockage ad lateral coecto, respectvely. Table 1 gves majorty odds results for 6 sgature types of 3 ppe codtos 5 frequecy bads. Table 2 shows the classfcato results usg the adapted KNN algorthm. Estmatos of testg data were based o the majorty votes ad were correct of all sgatures. The majorty

8 votes were calculated by usg equato (6) ad the stadard devatos were calculated usg followg equato: 1 s x x N 2 ( ), N 1 1 N x N 1 x (7) where x are data samples of oe sgature, x s the mea of these data. Table 1. Majorty odds(%) of 5 frequecy bads Sgature types Freq Hz Freq Hz Freq Hz Freq Hz Freq Hz [1-25] [2-35] [3-45] [4-55] [5-65] PE of empty ppe 1% 1% 1% 1% 85.7% PE wth ope lateral coecto 71.4% 1% 1% 57.1% 28.6% PE wth blockage sde ppe 1% 1% 1% 1% 42.8% PE wth blockage sde ad ope LC 85.7% 1% 71.4% 57.1% 28.6% BK sde ppe 14.3% 1% 1% 1% 85.7% LC of empty ppe 1% 1% 42.8% 85.7% 42.8% Table 2. Sgature classfcato results usg KNN algorthm Sgature types Majorty votes Estmato Stadard Devato PE of empty ppe 97.2% PE 1.65 PE wth ope lateral coecto 71.4% PE.74 PE wth blockage sde ppe 88.6% PE 1.16 PE wth blockage sde ad ope LC 65.7% PE.62 BK sde ppe 8.% BK.56 LC of empty ppe 74.3% LC.52

9 V. Cocluso A K-earest eghbours (KNN) algorthm has bee developed ad used for ppe codto classfcato. Ths system s capable of detfyg ppe objects of a water-flled ppe usg ts acoustc sgatures. 3 ppe codtos of 2 water levels were studed ths work cludg: empty ppe, ppe wth ope lateral coecto ad blockage sde ppe. Ppe ed, lateral coecto ad blockage sgatures were obtaed ad used classfcato. Sgatures frequecy 1Hz to 1Hz were fltered 2 frequecy bads to mprove the resoluto of classfcato, a frequecy rage of 15Hz-45Hz of 5 bads was foud to be more useful to dscrmate patters, the amout of data samples calculato ca be reduced usg these 5 frequecy bads stead of the orgal 2 bads, as a result, the classfcato results ca be more sestve to the codto chage ad the calculatos are more effcet. The acoustc eergy has bee used as the ma feature the classfcato process. It has bee foud a useful characterstc whch eables dscerble dfferece betwee sgatures to be measured. The proposed system has proved to be relable to eable to dscrmate typcal codtos a partlyflled ppe. Other acoustc parameters wll be studed the future to provde addtoal dmesos for the classfcato process ad mprove ts robustess ad resoluto. Ackowledgemets The authors would lke to ackowledge the work of ther techcas at the Mechacal Egeerg Workshop ad the Hydraulcs Laboratory the School of Egeerg at the Uversty of Bradford. Refereces 1. BAl, M. Tareq (21). 'Developmet of Acoustc Sesor ad Sgal Processg Techque', PhD thess, The Uversty of Bradford. 2. Rchard. O.(2). 'Patter Classfcato', NY,USA: Wley-Iterscece. 3. Yella, S. (26). 'Codto motorg usg patter recogto techques o data from acoustc emssos'. Proceedgs of the 5th Iteratoal Coferece o Mache Learg ad Applcatos. 4. Bshop, C. M. (1995). 'Bayesa regresso ad classfcato'. Computer ad Systems Sceces, volume 19.