Bektas, Gokhan

Bektaş, Gökhan

Boz, Talha

Chelliah, Pandian

Deniz, A.

Kuelen, C.

Melemez, Fazlı Fatih

Melemez, Fazli Fatih

Ozturk, Cem

Suleman, A.

Yıldız, Mehmet

Yildiz, Mehmet

Öztürk, Cem

English

Sabanci University Research Database

 italumiS o buS lartcepS fo seidutS n tract noi  Ba o noitasnepmoC erutarepmeT des  f GBF  sneS o tinoM htlaeH larutcurtS rof r o gnir   .P  HAILLEHC , A. INED Z  , C  . K ELUE N, .G   ,SATKEB T. B ZO , F. C ,ZEMELEM  . TZO RU A ,K . ELUS M .M dna NA  LIY D ZI     ABSTRACT   rebiF B targ ggar i gn tpo era )sGBF( s i ac l t rosnes desab rebif h  ta era  widely u es d f arutcurts ro l aeh lth m no i ot ring. ,revewoH  eht  niarts   fo stnenopmoc erutarepmet dna  eht  tnemerusaem  era  oc upl de . sihT  ap p re  sucsid s se  a ohtem d to de lpuoc e eht  t ow  pa ar meters su ing spectral us btr ca tion.   I TN RODUCTION  lediw era )sGBF( sgnitarg ggarB rebiF y  desu f aeh larutcurts ro l  ht m no i gnirot  of iub ldi gn s  , bri gd es  , dam  s a dn  to her in ud strial structu er s [1].  ehT F  GB i snes s i vit e to htob  erutarepmet  orht( u hg  tneiciffeoc  fo  lamreht  ex noisnap  dna  eht omr - citpo  ffeoc i )tneic  dna  niarts  t( h hguor  niarts  citpo  )tneiciffeoc . ehT  d tnempoleve  of reporp  sol tu ions cap ba l o e f decouplin t g emperatu a er nd strain responses of FB  G sensors from  o eno neeb sah rehto hcae f o segnellahc niam eht f nummoc cifitneics eht ity t ecnis he in tcudort ion of a srosnes GBF n ifitneics tsav fo si d c i tn ere  ts even now. I ht n i pap s er, ew  rovaedne  ot  noitasnepmoc erutarepmet sdrawot noitulos lacitcarp erom a edivorp  by isu ng an GBF  ni  co noitanibm  htiw  a elpuocomreht  by itpoda ng ht e lartceps  sub rt action methodol go y.   naidnaP  C ,hailleh  Atam na  ,zineD  T ahla  Bo ,z  oG k nah  eB k ,sat  hitaF  M ele m ze  , Cem O utz rk, Mehme  t Y li diz decnavdA  oC m etisop s dna  yloP m re  ecorP ss ni g aL b otaro ry, lucaF ty of Eng ni eer ni g a dn  aN tura  l S eic n ec s  , S ba anc U i n vi ers ti y  , Istanbul  , Turkey  C esa y Keu el n  , Afza  l Suleman De ap mtr en  t o M f e hc an ci a  l eng ni eering, Un vi ers ti y of V tci oria, Victoria, BC ,.  CanadaTHEORY   In this section, we briefly describe the fundamental equations governing the FBG sensor, explain methodologies adopted in literature to decouple the strain and temperature sensitivity, and finally, the concept of the spectral subtraction. An FBG sensor consists of periodic modulation of refractive index in the fiber such that the counter-propagating modes are coupled. The Bragg wavelength is given by [2]   2B nλ = Λ           1 where n is the effective refractive index of the fiber core, and Λ  is the period of fiber Bragg grating. The effect of temperature and strain on the change in the Bragg wavelength is given by   ( ),B B o TK K Tελ λ ε∆ = ∆ + ∆         2  The temperature compensation can be achieved if Eq. 2 is manipulated to eliminate one of the constants. This can be realized in the following ways. Consider the case, where 0Kε =   or 0TK =  . If in Eq. 2, the temperature sensitivity can be made zero, the equation is then only dependent on the strain. Usually, this is achieved by enclosing the FBG in a glass tube. The strain sensitivity component can be made zero by enclosing the fiber in a metal tube and leaving the fiber ends free to move. Alternative to the above methodology, Eq. 2 can also be solved by setting up two equations to calculate the two variables, namely, Kε  and TK . The best demonstration of this method was described in [3] where M.G.Xu et al., used superimposed FBGs and in [4] where two different FBGs were used. Each of the two FBGs has its own temperature and strain coefficients represented by subscripts 1 and 2. Eq. 2 thus could be rewritten as  , 1 11, 2 2200B o TBB o TBK KK Kεελλλλ∆     =     ∆           3  In literature, the above equations have been implemented and solved by using two independent FBGs, or one FBG and one long period grating, or two FBGs arising out of a single FBG written on a single polarizing maintaining fiber. Other methodologies using encapsulated FBGs, or using FBGs welded to bimetals have been also tried in literature with varying success [5-7].   In the following, we describe the spectral subtraction method which is core to the approach of decoupling methodology adopted in this paper. Spectral subtraction is a widely used methodology for reducing noise in signals, and especially useful in audio processing [8]. The process is described below. Consider a noisy signal ( )y t  consisting of noise free signal ( )x t  and the noise ( )n t  in time domain  ( ) ( ) ( )y t x t n t= +          4  Converting the signal to frequency domain, one can obtain  ( ) ( ) ( )Y X Nω ω ω= +         5  Assume that we subtract only the magnitude part of the FFT ( )N ω  and retain the phase of the signal ( )Y ω . The ( )N ω  is also known as the estimation of the noise. The noise free signal is obtained by  ( ) ( ) ( )X Y Nω ω ω= −         6  In audio processing, the noise estimation could be made by measuring the noise without the actual signal. In our temperature and strain decoupling approach, the strain can be treated as signal of interest while the temperature as noise.   SIMULATION  Here, the application of spectral subtraction for different temperature variation is presented. The strain is assumed to vary as sinusoid while the temperature is considered to have sinusoid, ramp, and square wave forms. We demonstrate that the frequency components arising out of the different variation patterns of temperature can be filtered in the frequency domain using spectral subtraction. We explain spectral subtraction methodology for the sinusoid variation in detail, and to avoid the redundancy, the procedure for the ramp and square wave forms is not explained in detail since it is similar to the one discussed for sinusoid variation.  Sine Wave Form: Eq.2 describes the change in wavelength shift as a function of temperature and strain. Generally, the strain sensitivity of an FBG sensor 1.2 /K pmε µε=  while the temperature sensitivity 10 /oTK pm C= . The temperature varies as a sinusoid from 18 °C to 42 °C with the frequency of 0.03 .Tf Hz= as given in Eq.7, and is represented in Fig.1a. The strain varies as a sinusoid from +9 µε to -9 µε with the frequency of 4 .f Hzε = as shown in Eq.8 and in Fig.1b. The positive and negative values indicate the tension and the compression, respectively and thus, the periodic variation corresponds to periodic tension and compression. ( ) ( )1 1sin 2 sin 2 TT A f t B f t rnεπ π= + +       7 ( ) ( )2 2sin 2 sin 2 TA f t B f t rnεε π π= + +       8  where rn  is the random noise. In real life situation, sometimes, temperature modulation leads to strain modulation. Therefore, we have added a 1% of the strain to vary the signal at the same frequency as the temperature. Due to the autogenous heating, the temperature can vary with the same frequency as the strain modulation. Hence, we have added 10% of the temperature amplitude to vary at the frequency of the strain. These additions demonstrate the robustness of the simulation. The FBG signal is the additive of Eq. 7 and Eq. 8 as seen in Eq. 2 and as represented in Fig.1c. The frequency domain based spectral methodology for decoupling strain and temperature components is described below.   (a)  (b)  (c)  (d)  Figure 1 : A simulation test for spectral subtraction where in (a) the strain varies as a sinusoid (high frequency), (b) the temperature varies as a sinusoid (low frequency), (c) the high frequency strain is modulated by the low frequency temperature, and (d) the strain measurement is reconstructed. 1. The FBG signal is converted to frequency domain. The FFT of the FBG signal is shown in Fig.2a. It consists of both 4Hz strain signal and 0.03Hz temperature signal. 2. The temperature is converted to wavelength using the temperature sensitivity. 3. The temperature calculated in step 2 is converted into frequency domain. The FFT of the temperature signal is shown in Fig.2b. The signal consists of strong 0.03 Hz temperature signal, and also a weak 4Hz signal. 4. In the frequency domain, the temperature signal is subtracted from the FBG signal, Fig.2c. It could clearly be seen that the 0.03 Hz signal is no longer present in the frequency domain. 5. The temperature independent signal is obtained by obtaining the inverse FFT of the subtracted signal, Fig.1d.  (a)  (b)  (c)  Figure 2: Spectral subtraction in the frequency domain: (a) the FFT of the FBG data, (b) the FFT of the temperature data, and (c) the subtraction of the FFT of the FBG and the temperature data.  Ramp Wave Form: While strain is varied as a sinusoid in cyclic fatigue tests, in most real life cases, temperature does not vary as a sinusoid. One common exception is the diurnal variation of temperature. However, in experiments, the temperature often varies linearly with time, which either increases or decreases, and therefore can be represented as ramp. It can be seen from Fig.3 that the temperature compensation can be achieved in the same way as in the case of sine variation.   (a)  (b)  (c)  (d) Figure 3: A simulation test for spectral subtraction where in (a) the strain varies as a sinusoid high frequency), (b) the temperature varies as a ramp (low frequency), (c) the high frequency strain is modulated by the low frequency temperature, and (d) the strain measurement is reconstructed.  Square Wave Form: It is possible in real life studies that there is a sudden increase or decrease in temperature and the temperature remains constant at this new higher or lower temperature. This could be represented as a square wave. In Fig.4, we find that this could also be eliminated in the frequency domain. The random variation in temperature has been included in all the three variation patterns of temperature. Thus we can say that the temperature can be compensated in FBG sensors if we know the temperature pattern using any temperature sensor.  (a)  (b)  (c)  (d) Figure 4:  A simulation test for spectral subtraction where in (a) the strain varies as a sinusoid (high frequency), (b) the temperature varies as a square (low frequency), (c) the high frequency strain is modulated by the low frequency temperature, and (d) the strain measurement is reconstructed.  CONCLUSION   In structural health monitoring using FBG sensors, the temperature and strain variations have to be decoupled for successful and true monitoring of the strain field. In this work, we have successfully demonstrated that the spectral subtraction method can be used to achieve this objective. The advantage of this method over the others described in literature is that only the pattern of temperature variation has to be monitored as against the magnitude of variation which is required by other methods. This approach also leads to reduction in error which will be demonstrated in our future publications.  Acknowledgment: The funding provided by the Scientific and Technological Research Council of Turkey (TUBITAK) for the project 112M357 is gratefully acknowledged. REFERENCES  1. Chan T H T, Yu L, Tam H Y, Ni Y Q, Liu S Y, Chung W H and Cheng L K, “Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation, Engineering StructuresVolume 28, 648-6 (2006) 2 A. Kersey, M. Davis, H. Patrick, M. LeBlanc, K. Koo, C. Askins, M. Putnam, and E. Friebele, “Fiber grating sensors," Lightwave Technology, Journal of 15, 1442 -1463 (1997). 3. M. Xu, L. Dong, L. Reekie, J. Tucknott, and J. Cruz, “Temperature-independent strain sensor using a chirped bragg grating in a tapered optical fibre," Electronics Letters 31, 823 -825 (1995).  4. K. Li and Z. Zhou, “A high sensitive fiber bragg grating strain sensor with automatic temperature compensation," Chinese Optics Letters 7, 191-193 (2009). 5. S. Oh, W. Han, U. Paek, and Y. Chung, “Discrimination of temperature and strain with a single fbg based on the birefringence effect," Opt. Express 12, 724-729 (2004). 6. S. James, M. Dockney, and R. Tatam, “Simultaneous independent temperature and strain measurement using in-fibre bragg grating sensors," Electronics Letters 32, 1133 -1134 (1996). 7. H. Patrick, G. Williams, A. Kersey, J. Pedrazzani, and A. Vengsarkar, “Hybrid fiber bragg grating/long period fiber grating sensor for strain/temperature discrimination," Photonics Technology Letters, IEEE 8, 1223  -1225 (1996). 8. B. L. Sim, Y. C. Tong, J. Chang, and C. T. Tan, “A parametric formulation of the generalized spectral subtraction method," Speech and Audio Processing, IEEE Transactions on 6, 328 -337 (1998).                       

A high sensitive fiber bragg grating strain sensor with automatic temperature compensation,&quot;

A parametric formulation of the generalized spectral subtraction method,&quot; Speech and Audio Processing,

Discrimination of temperature and strain with a single fbg based on the birefringence effect,&quot;

Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation,

Hybrid fiber bragg grating/long period fiber grating sensor for strain/temperature discrimination,&quot;

Simultaneous independent temperature and strain measurement using in-fibre bragg grating sensors,&quot;

Temperature-independent strain sensor using a chirped bragg grating in a tapered optical fibre,&quot;

Simulation studies of spectral subtraction based temperature compensation of FBG sensor for structural health monitoring based on principal component analysis

http://research.sabanciuniv.edu/23543/1/0478.pdf

Simulation studies of spectral subtraction based temperature compensation of FBG sensor for structural health monitoring based on principal component analysis

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