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Warianty tytułu
Heurystyczna metoda wykrywania i lokalizowania usterek z wykorzystaniem elektromagnetycznych przetworników akustycznych
Języki publikacji
Abstrakty
The objective of this paper is to demonstrate a novel signal processing for detection, identification and flaw sizing of structural damage using ultrasonic testing with Electromagnetic Acoustic Transducers (EMATs). Damage detection involves the recognition of a defect that exists within a structure. Damage location is the identification of the geometric position of the defect. Defect classification is the cluster of the damage type into multiple damage scenarios. In the absence of external interferences, a good measure of detectability of a flaw is its signal-to-noise ratio (SNR). Although the SNR depends on various parameters such as electronics used, material properties, e.g. homogeneity and damping, and flaw size, it can be improved using advanced signal processing. The main scientific novelties presented in this paper focus on filtering signal noise through advanced digital signal processing; incorporating wavelet transforms for image and signal representation enhancements; investigating multi-parametric analysis for noise identification and defect classification; studying attenuation curves properties for defect localisation improvement and flaw sizing and location algorithm development.
Celem niniejszego artykułu jest omówienie nowatorskiego sposobu przetwarzania sygnałów w celu wykrywania, identyfikacji i oceny uszkodzeń strukturalnych przy użyciu ultrasonograficznych testów za pomocą elektromagnetycznych przetworników akustycznych (EMAT). Wykrywanie uszkodzeń polega na rozpoznaniu istniejących defektów wewnątrz danej struktury. Lokalizacja uszkodzeń sprowadza się do identyfikacji geometrycznego położenia defektu. Klasyfikacja defektu to klaster typu uszkodzenia w wielu scenariuszach uszkodzeń. W przypadku braku zewnętrznych zakłóceń, dobrym wskaźnikiem wykrywalności błędu jest stosunek sygnału do szumu (SNR). Pomimo tego, że SNR zależy od różnych parametrów, takich jak użyta elektronika, właściwości materiału, np. jednorodność i tłumienie, a także wielkość wady, wskaźnik ten można poprawić przy użyciu zaawansowanego przetwarzania sygnałów. Główne nowe zagadnienia naukowe przedstawione w niniejszym artykule skupiają się na filtrowaniu szumu sygnału za pomocą zaawansowanego przetwarzania sygnału cyfrowego, w tym wykorzystując transformaty falkowe w celu ulepszenia obrazu i sygnału; badanie analizy wieloparametrycznej w celu identyfikacji szumów i klasyfikacji defektów; badanie właściwości krzywych osłabiania w celu sprawniejszego wykrywania i oceny wad oraz rozwoju algorytmu lokalizacji.
Czasopismo
Rocznik
Tom
Strony
493--500
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
- Ingenium Research Group European University of Madrid Tajo street, c Building, c4 office, 28670, Villaviciosa de Odon Madrid, Spain
autor
- Ingenium Research Group Castilla-la Mancha University Politecnic Building, Camilo José cela Street, 13071 Ciudad Real, Spain
autor
- Ingenium Research Group Castilla-la Mancha University Politecnic Building, Camilo José cela Street, 13071 Ciudad Real, Spain
autor
- Brunel Innovation Centre TWi, Granta Park, Granta Park Cambridge, cB21 6al United Kingdom
autor
- Brunel Innovation Centre TWi, Granta Park, Granta Park Cambridge, cB21 6al United Kingdom
autor
- Brunel Innovation Centre TWi, Granta Park, Granta Park Cambridge, cB21 6al United Kingdom
autor
- School of Metallurgy and Materials University of Birmingham Edgbaston Birmingham, B15 2TT United Kingdom
Bibliografia
- 1. Aktas M, Turkmenoglu V. Wavelet-based switching faults detection in direct torque control induction motor drives. IET Science, Measurement & Technology 2010; 4(6): 303-310, https://doi.org/10.1049/iet-smt.2009.0121.
- 2. Canal M R. Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals. Journal of Medical Systems 2010; 34(1): 91-94, https://doi.org/10.1007/s10916-008-9219-8.
- 3. Chen Y. Acoustical transmission line model for ultrasonic transducers for wide-bandwidth application. Acta Mechanica Solida Sinica 2010; 23(2): 124-134, https://doi.org/10.1016/S0894-9166(10)60014-6.
- 4. Dai D, He Q. Structure damage localization with ultrasonic guided waves based on a time–frequency method. Signal Processing 2014; 96: 21-28, https://doi.org/10.1016/j.sigpro.2013.05.025.
- 5. Ruiz R, Garcia F P, Dimlaye V. Maintenance management of wind turbines structures via MFCs and wavelet transforms. Renewable and Sustainable Energy Reviews 2015; 48: 472-482, https://doi.org/10.1016/j.rser.2015.04.007.
- 6. Ruiz R, Garcia, F P, Dimlaye V, Ruiz D. Pattern recognition by wavelet transforms using macro fibre composites transducers. Mechanical Systems and Signal Processing 2014; 48(1): 339-350.
- 7. Dong Y, Shi H, Luo J, Fan G, Zhang C. Application of wavelet transform in MCG-signal denoising. Modern Applied Science 2010; 4(6): 20, https://doi.org/10.5539/mas.v4n6p20.
- 8. Eristi H. Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system. Measurement 2013; 46(1): 393-401, https://doi.org/10.1016/j.measurement.2012.07.014.
- 9. García F P, García I. Principal component analysis applied to filtered signals for maintenance management. Quality and Reliability Engineering International 2010; 26(6): 523-527, https://doi.org/10.1002/qre.1067.
- 10. García F P, Chacón J M, Tobias A M. B-Spline approach for failure detection and diagnosis on railway point mechanisms case study. Quality Engineering 2015; 27(2): 177-185, https://doi.org/10.1080/08982112.2014.933980.
- 11. García F P, Pedregal D J, Roberts C. Time series methods applied to failure prediction and detection. Reliability Engineering & System Safety 2010; 95(6): 698-703, https://doi.org/10.1016/j.ress.2009.10.009.
- 12. Genovese L, Neelov A, Goedecker S, Deutsch T, Ghasemi S A, Willand A. Schneider R. Daubechies wavelets as a basis set for density functional pseudopotential calculations. The Journal of chemical physics 2008; 129(1), https://doi.org/10.1063/1.2949547.
- 13. Jia M T, Wang Y C. Application of wavelet transformation in signal processing for vibrating platform. Journal-Shenyang Institute of Technology 2003; 22(3): 53-55.
- 14. Light-Marquez A, Sobin A, Park G, Farinholt K. Structural damage identification in wind turbine blades using piezoelectric active sensing. Structural Dynamics and Renewable Energy 2011; 1: 55-65, https://doi.org/10.1007/978-1-4419-9716-6_6.
- 15. Ljung L. System Identification Toolbox for Use with {MATLAB} 2007.
- 16. Marquez F P. An approach to remote condition monitoring systems management. Railway Condition Monitoring. The Institution of Engineering and Technology International Conference 2006: 156-160, https://doi.org/10.1049/ic:20060061.
- 17. Márquez F P. A New Method for Maintenance Management Employing Principal Component Analysis. Structural Durability & Health Monitoring 2010; 6(2): 89-99.
- 18. Márquez F P, Muñoz J M. A pattern recognition and data analysis method for maintenance management. International Journal of Systems Science 2012; 43(6): 1014-1028, https://doi.org/10.1080/00207720903045809.
- 19. Márquez F P, Pardo I, Nieto M. Competitiveness based on logistic management: a real case study. Annals of Operations Research 2013: 1-13.
- 20. Márquez F P G, Pedregal D J, Roberts C. New methods for the condition monitoring of level crossings. International Journal of Systems Science 2015; 46(5): 878-884, https://doi.org/10.1080/00207721.2013.801090.
- 21. Márquez F P, Pérez, J M, Marugán A P, Papaelias M. Identification of critical components of wind turbines using FTA over the time. Renewable Energy 2016; 87(2): 869–883, https://doi.org/10.1016/j.renene.2015.09.038.
- 22. Márquez F P, Tobias A M, Pérez J M, Papaelias M. Condition monitoring of wind turbines: Techniques and methods. Renewable Energy 2012; 46: 169-178, https://doi.org/10.1016/j.renene.2012.03.003.
- 23. Marugán A P, Márquez F P. A novel approach to diagnostic and prognostic evaluations applied to railways: A real case study. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2016; 230(5): 1440-1456, https://doi. org/10.1177/0954409715596183.
- 24. Morsi W G, El-Hawary M E. Novel power quality indices based on wavelet packet transform for non-stationary sinusoidal and non-sinusoidal disturbances. Electric Power Systems Research 2010; 80(7): 753-759, https://doi.org/10.1016/j.epsr.2009.11.005.
- 25. Muñoz J, Márquez F P, Papaelias M. Railroad inspection based on ACFM employing a non-uniform B-spline approach. Mechanical Systems and Signal Processing 2013; 40(2): 605-617, https://doi.org/10.1016/j.ymssp.2013.05.004.
- 26. Nieto N, Marcela D. The use of the discrete Wavelet transform in the reconstruction of sinusoidal signals. Scientia et Technica 2008; 38: 381-386.
- 27. Papaelias M, Cheng L, Kogia M, Mohimi A, Kappatos V, Selcuk C, García F P, Gan T H. Inspection and Structural Health Monitoring techniques for Concentrated Solar Power plants. Renewable Energy2016; 85: 1178-1191, https://doi.org/10.1016/j.renene.2015.07.090.
- 28. Pedregal D J, García F P, Roberts C. An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions. Annals of Operations Research 2009; 166(1): 109-124, https://doi.org/10.1007/s10479-008-0403-5.
- 29. Peng Z K, Chu F L. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing 2004; 18(2): 199-221, https://doi.org/10.1016/S0888-3270(03)00075-X.
- 30. Pérez, J. M. P., Márquez, F. P. G., & Hernández, D. R. (2016). Economic viability analysis for icing blades detection in wind turbines. Journal of Cleaner Production, 135, 1150-1160, https://doi.org/10.1016/j.jclepro.2016.07.026.
- 31. Pliego,A, García F P, Lorente J. Decision making process via binary decision diagram. International Journal of Management Science and Engineering Management 2015; 10(1): 3-8, https://doi.org/10.1080/17509653.2014.946977.
- 32. Su Z, Ye L. Identification of damage using Lamb waves: from fundamentals to applications Springer Science & Business Media 2009; 48.
- 33. Wang X, Peter W T, Mechefske C K, Hua M. Experimental investigation of reflection in guided wave-based inspection for the characterization of pipeline defects. NDT & E International 2010; 43(4): 365-374, https://doi.org/10.1016/j.ndteint.2010.01.002.
- 34. Wu J D, Liu C H. Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applications 2008; 35(3): 1200-1213, https://doi.org/10.1016/j.eswa.2007.08.021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-61201f59-c46c-4ef9-bb9b-233e27756c43