International Journal of Mathematics and Computational Science
Articles Information
International Journal of Mathematics and Computational Science, Vol.1, No.4, Aug. 2015, Pub. Date: Jun. 2, 2015
Fourier Transform in the Application of Power Plants Pipelines Defect Detection
Pages: 183-187 Views: 1973 Downloads: 688
[01] Saeedreza Ehteram, Control Expert of Wind Powerplants Engineering Department, MAPNA Electric & Control, Engineering & Manufacturing (MECO), Karaj, Iran.
[02] Seyed Zeinolabedin Moussavi, Electrical and Computer Engineering Faculty, Shahid Rajee Teacher Training University, Lavizan, Tehran, Iran.
Determination of power plants pipelines defects is an important task in industry. This study provides a simple way to classify these defects by the use of neural network also FT (Fourier Transform) is applied to have an analysis based frequency domain and data reduction on MFL (Magnetic Flux Leakage) database. The network receives in input a matrix of defects that are derived from a simulator formula that will be explained in follow. The aim of this research approach is the audit ability for safe or non safe material in pipelines and provides a binary output for indicating whether defect is recognized or not. The network proposed is MLP (multilayer perceptron) with strictly local connections. The first layer performs local linear operations, while the second has a non linear functionality. Result shown that this procedure could be used as an appropriate solution for pipelines defect detections.
Fourier Transform, Non-Destructive Testing, Magnetic Flux Leakage, Multilayer Perceptron
[01] Saeedreza Ehteram, "BELBIC - An Intelligent Controller based defect detection of power plants flow pipeline from MFL signals". The 3rd Conference on Thermal Power Plant IPG’2011- October 18-19, 2011 Amirkabir University of Technology, Tehran, Iran.
[02] Saeedreza Ehteram, Alborz Rezazadeh Sereshkeh, Seyed Zeinolabedin Moussavi, Ali Sadr, Ali Akbar Jalali, Utilizing a Pattern Recognition Controller and Linear Discriminate Analysis for MFL Defect Detection. JCIT 4(2009), pp.11-19.
[03] A. Bergamini, Nondestructive testing of stay cables, IABSE conference on cable-supported bridges(2001), pp. 312–313.
[04] A. Bergamini, Nondestructive testing of stay cables field experience in South East Asia, Third World conference on structural control vol2.(2002), pp. 1057–1064.
[05] M. Afzal and S. Upda, Advanced signal processing of magnetic flux leakage data obtained from seamless steel pipeline, NDT&E Int (2002) (7), pp. 449–457.
[06] P. Ramuhalli, L. Udpa and S.S. Udpa, Electromagnetic NDE signal inversion by function-approximation neural networks, IEEE Trans Magnetics (2002) (6), pp. 3633–364.
[07] R.R. da Silva, S.D. Soares, L.P. Caloba, M.H.S. Siqueira and J.M.A. Rebello, Detection of the propagation of defects in ressurized pipes by means of the acoustic emission technique using artificial neural networks, Insight 48 (2006) (1), pp. 45–51. Full Text via Cross Ref. View Record in Scopus Cited By in Scopus (3).
[08] C Mandache, B Shiari and L Clapham"Defect separation considerations in magnetic flux leakage inspection" Insight Vol 47 No 5 May 2005 pp.
[09] D.E. Bray, Nondestructive evaluation (revised ed.), CRC Press, Boca Raton, FL (1997).
[10] R. Christen, A. Bergamini and M. Motavalli, Three-dimensional localization of defects in stay cables using magnetic flux leakage methods, J Non Destructive Eval 22 (2004) (3), pp. 93–101.
[11] Martin Golz, David Sommer," The Performance of LVQ Based Automatic Relevance Determination Applied to Spontaneous Biosignals, KES 2006, Bournemouth, UK, October 9-11, 2006. Proceedings, Part III, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2006. pp. 1256-1263.
[12] Hiroshi Wakuya, Hiroyuki Harada, Katsunori Shida "An architecture of self-organizing map for temporal signal processing and its application to a Braille recognition task" Wiley Periodicals, Inc. Syst Comp Jpn, 38(3): 62- 71, 2007.
[13] Saeedreza Ehteram, Seyed Z. Moussavi "Semantic Supervised clustering to land Extraction on satimage database" journal of Global engineering , science and technology (GESTS), Seul korea March 2007, pp.117-125.
[14] Saeedreza Ehteram, Ali Sadr, Seyed zeinolabedin Mousavi "Rapid face recognition by regional feature extraction ", INISTA 2007 conference Istanbul Turkey20 – 23 June2007, pp. 262-269, 2007.
[15] R. Ebrahimpour, S. R. Ehteram, E. Kabir, "Face Recognition by Multiple Classifiers, a Divide-and-Conquer Approach ", Lecture Note in Computer Science (LNCS), vol. 3686, pp. 225-232, September 2005..
[16] R. Ebrahimpour, Seyed Zeinolabedin Moussavi, and Saeedreza Ehteram "Multiple Binary Classifier Fusion (MBCF) in Application of Satimage Database" IASTED from proceeding (522) Applied Simulation and Modeling 2006-Greece.
[17] Y. Chung Bang, L. Jong Won, K. Jae Dong and M. Kyung Won, Damage estimation method using committee of neural networks, Proceedings of the SPIE—the international society for optical engineering vol. 5047 (2003), pp. 263–274.
[18] Qi Jiang, Qingmei Sui, Nan Lu, Paschalis Zachariades, Jihong Wang.” Detection and estimation of oil gas pipeline Corrosion defects "
[19] Dobmann G and H¨oller P 1980 Research Techniques in Nondestrucrive testing R. S. Sharp (New York: Academic) vol IV, pp.39–69.
[20] Shcherbinin V E and Pashagin A I 1972 Defektoskopyia pp.874–82.
[21] Forster F 1986 NDT Int. 19 3–13.
[22] Edwards C and Palmer S B 1986 J. Phys. D: Appl. Phys. 196pp.57–73.
[23] Mandal K and Atherton D L 1998 J. Phys. D: Appl. Phys. 31 pp.3211–17.
[24] Uetake I and Saito T 1997 NDT & E Int. 30 pp.371–7.
[25] Hwang J H and Lord W 1975 J. Testing Eval. 3 pp.21–5.
[26] LordWand Hwang J H 1977 Br. J. Non-dest. Testing 19 pp.14–18.
[27] Lord W, Bridges J M, Yen W and Palanisamy R 1978 Mater.Eval. 36 pp.46–54.
[28] Atherton D L and Daly M G 1987 NDT Int. 20 pp.235–8.
[29] Patel U and Rodger D 1995 IEEE Trans. Magn. 31 pp.2170–3.
[30] Altschuler E and Pignotti A 1995 NDT & E Int. 28 pp.35–40.
[31] Philip J, Rao C B, Jayakumar T and Raj B 2000 NDT & E Int. 33 pp.289–95.
[32] M.Turk, A. Pentland Eigenfaces for Recognition", Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, 1991.
[33] Duda, R.O. and Hart, P.E., Pattern Classification and Scene Analysis, John Wiley & Sons, 1973.
[34] Ken cabeen, peter gent."Image compression and the discrete cosine transform" math45 collage of Redwoods, pp1,2.
[35] S. Chandrasekaran, B.S. Manjunath, Y.F. Wang, J. Winkeler, and H. Zhang,An Eigensapce update algorithm for image analysis,", to appear in the journal Graphical Model and Image Processing, 1997.
[36] M.R. Jamali , A. Arami, M. Dehyadegari, C. Lucas, Z. Navabi, Emotion on FPGA: Model driven approach” ESWA 3156 No. of Pages 10-20 Oct. 2008.
[37] "PCA and MLP Combination in the Application of Power Plants Flow Pipelines Defect Detection" Saeedreza Ehteram, Seyed z Moussavi, ARTIFICIAL INTELLIGENCE AND APPLICATIONS 08/2014.
[38] "Simple Classification of mathematical simulated defects of Power plants pipelines" Saeedreza Ehteram, Borzoo Afkham, Seyed z Moussavi, Non-Destructive Testing 05/2013.
[39] "DEFECT DETECTION OF POWER PLANTS FLOW PIPELINES BY A COMBINATION OF NEURAL NETWORKS" Saeedreza Ehteram, Borzoo Afkham, British Academic Journals 12/2012.
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