ANALYSIS OF OTDR MEASUREMENT DATA WITH WAVELET TRANSFORM. Hüseyin ACAR *

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1 ANALYSIS OF OTDR MEASUREMENT DATA WITH WAVELET TRANSFORM Hüseyin ACAR * Department of Electrical and Electronics Engineering, Faculty of Engineering, Dicle University * hacar@dicle.edu.tr ABSTRACT: In this study, Wavelet transform is used to determine the possible faults (events) and events locations in fiber optic cable, and it is aimed to analyze the OTDR measurement data. The OTDR measurement data, which are used in this study, measured with JDSU MTS-2000 OTDR device from a fiber optic cable in Point-to-point (PtP) Passive Optical Network (PON). These OTDR measurement data include reflective and nonreflective events. It is shown that Wavelet transform is successful in detection of events and their locations in OTDR measurement data. Key words: OTDR measurement data, Wavelet transform, fiber event detection and localization. 1. Introduction In recent years, with the developments in communication technologies, the density of information traffic has increased. Accordingly, the use of fiber optic cables in communication lines is becoming widespread because of the immunity to electromagnetic noise, both in terms of transmission speed. Optical time domain reflectometer (OTDR) has been widely used since 1976 to measure the attenuation distribution along optical fiber [1]. The OTDR is the most common technique available to characterize the weaknesses in fiber communication links, to determine the location of failures, and to determine the exact location of the fracture in the fiber optic cable when no damage is observed in the cable lid. OTDRs are used to determine fiber length, end to end loss, location of optical loss, and to measure the reflections of elements along the fiber [2]. The OTDR works based on the Rayleigh backscatter principle. The basic idea is to send a short pulse of light along the fiber and examine the time dependent response of the back scattered signal. The amplitude of the light emitted back to the normal field is exponentially weak. Any sudden change in the level of backscattering of the fiber is often referred to as an 'event'. However, in the OTDR curve, the nonreflective phenomenon occurs in the form of a sudden drop, while the mechanical event occurs as a peak due to the connector and the different fiber refractive indices [3]. An OTDR output consists of the distance (km) and power loss (db) parameters. An OTDR graph shows x-axis distance and y-axis shows power loss or weakening. An exemplary OTDR mark used in the study is shown in fig.1. 1

2 Figure 1. A sample OTDR signal used in the study OTDR devices are manually connected to the faulty cable by operators in order to locate the fault when a fault occurs. This traditional technique delays the restoration time of the network system. To reduce the time delay, one of the solutions is to continuously connect the OTDR to the system. However, since the OTDR is very expensive, this is not an effective cost-effective solution and it is impractical to incorporate these test devices into the system given the fact that fiber breakdown does not occur frequently. The point-to-point fiber cable failure can be monitored by analyzing the center frequency of the RF spectrum (FFT) of the central office [CO] wavelength [4]. Possible fiber faults in the optical network can be localized by calculating the correlation between pre-measured reference OTDR markings and measured OTDR markings [5]. As a result of a network failure caused by fiber failure in existing optical communication systems, it can be very difficult for telecommunications operators to return their systems to normal. They may face significant problems in locating the cable fault and finding the fiber fault along the optical cable. According to reports from the Federal Communications Commission (FCC), more than a third of service interruptions are caused by fiber cable problems [6]. Therefore, detecting fiber failure and restoring the cable will shorten service interruption times. 2. Method 2.1. Wavelet Transform Wavelet Transform (DD) is a very powerful tool used in many signal processing applications, enabling the mark to be analyzed with scalable time-frequency representation. The DD is based on a set of signals derived from a basic master wavelet by adjusting the time-delay and time-extension parameters. DD uses wide windows for low frequencies while using narrow windows for high frequencies. Large windows provide good frequency and low time resolution, while narrow windows provide good low-frequency resolution [7]. The calculation of all wavelet coefficients, each scale, will cause a very large workload and unnecessary amount of information. If scale and position are chosen in multiples of doubles, the method will be as accurate as before and much more effective. Such an analysis provides a Discrete Wavelet Transform. Discrete Wavelet Transform, 2

3 m ADD x( m, n) x( t) (2 t n) dt Is expressed by [8]. Where m is the frequency, n is the distance, x (t) is the sign and Ψ (t) is the wave. In this study, the wave 'haar' was used as the wave Ψ (t). In the wavelet transform, the signal is divided into a number of scales by multi resolution splitting operation. The multiple resolution decomposition process for the x (n) sign is shown in Fig. In Figure 2, the output of the first high-pass filter (g [.]) Forms the D1 detail coefficients and the sampled output of the low-pass filter (h [.]) Forms the A1 approximation coefficients. The approximate band A1 is again separated and this process continues as shown in Fig. 3 [9]. In our study, ADD 4th level detail coefficients (cd4) were used for incident detection. Figure 2. Decomposition of x [n] into approximate and detail bands by Wavelet Transform The wavelet analysis method is used to find discontinuities in the OTDR measurement data. The OTDR signal is subjected to wavelet transform and the coefficients are subjected to a threshold value filter to extract the high frequency information because the sharp changes are in the high frequency domain. 3. Findings The noisy true OTDR signal, which is used in the study and contains nonreflective phenomena, includes three nonreflective events at 0.2 db / km Rayleigh attenuation and optical linkages at , and meters with 0.093, and db losses respectively. The local maximums of the wavelet transform coefficients of the real OTDR data were found and OTDR event detection was attempted. In Fig.3, the results of Wavelet analysis of non-reflectivity events are given. With wavelet analysis, non-reflectivity events and localizations can be successfully detected. These three phenomena with low amplitudes seem to be distinguishable from noise. 3

4 Figure 3. Wavelet analysis of nonreflective events (three events in the OTDR mark above, Wavelet Transform detail coefficients at the bottom) Another OTDR signal with reflective events includes two reflective events with 0.2 db / km Rayleigh attenuation and optical losses of and meters, respectively, with and db losses. In Fig.4, the results of wavelet analysis of the reflective events are given. With wavelet analysis, events and localizations can be successfully detected. Figure 4. Wavelet analysis of reflective events (two events at the top OTDR mark, bottom Wavelet Transform detail coefficients) 4. Result In this work, it has been shown that event location can be detected from OTDR measurement data using Wavelet Transform. It has also been shown that both low-frequency reflective and nonreflective events in the fiber optic cable can be distinguished from noise and from the Rayleigh component in the OTDR data. 4

5 References [1] Usama, M., & Muhammad, S. S. (2013). Vector Indexing Algorithm for Post Processing of OTDR Data. 18th European Conference on Network and Optical Communications & 8th Conference on Optical Cabling and Infrastructure, Graz, Austria. [2] Anderson, D., & Beenem, G. (1998). Assessing Accuracy in OTDR Measurements. Electronics Engineer, 1-4. [3] Anritsu, (2015). OTDR module operation manual. Retrieved from [4] Wang Z. X., Sun X. F. Lin C. L., Chan C. K., & Chen L. K. (2004). A novel surveillance scheme for passive optical networks using spectral analysis. in Proc. ECOC [5] 5.Liu Z., Li M., Chan C. K., (2012). Fault localization in passive optical networks using OTDR trace correlation analysis. Optical Fiber Communication Conference and Exposition (OFC/NFOEC), and the National Fiber Optic Engineers Conference, vol., no., pp.1-3. [6] Masson B., (2002). Ensuring the Availability and Reliability of dark-fiber Networks. EXFO electro-optical Engineering Inc., p [7] Jaipradidtham, C., & Kinnares, V., (2005). Discrete wavelet transform - signal processing for harmonic analysis of electromagnetic transients in 500 kv single circuit transmission system. IEEE International Symposium on Communications and Information Technology, ISCIT 2005., pp [8] Erişti, H., Uçar A, Demir, Y., (2007). Destek vektör makineler ve dalgacık dönüşümü kullanarak güç sistemlerinde geçici rejim bozulmalarının sınıflandırılması. ElektrikElektronik, Bilgisayar, Biyomedikal Mühendisliği 12. Ulusal Kongresi, Eskişehir, [9] Acar, H., Akın, M., Yıldız, A., Eği, H., & Kırbaş, G. (2010). The classification of alertness level from EEG signals by using TMS320C6713 DSK and MATLAB. IEEE 18th Signal Processing and Communications Applications Conference, Diyarbakir, pp

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