Identification of Delamination in Composite Beams using the Fractal Dimension-Based Damage Identification Algorithm

• Autorzy: Katunin A. , Zuba M.

Identyfikatory

DOI

10.1515/fas-2017-0001

• Języki publikacji: EN

Abstrakty

EN

Damage detection and identification is one of the most important tasks of proper operation of technical objects and structures. It is, therefore, essential to develop efficient and sensitive methods of early damage detection. Delamination is the type of damage occurring in laminated composites that is one of the most dangerous and most difficult to detect. In this paper, the computational study was performed on the numerical data of the modal shapes of laminated composite beams with simulated delaminations in order to detect them using a fractal dimension-based approach. The obtained results allowed for improvement of detection accuracy as compared to previously applied wavelet-based approach. An additional benefit was decreasing the computational time. Basing on the obtained results it is reasonable to consider the presented approach as a promising alternative to currently applied signal processing methods used for supporting nondestructive testing of structures.

Słowa kluczowe

EN

structural damage identification; fractal dimension; non-destructive testing; composite structures; delamination

• Wydawca: Wydawnictwa Naukowe Sieci Badawczej Łukasiewicz - Instytutu Lotnictwa
• Czasopismo: Fatigue of Aircraft Structures
• Rocznik: 2017
• Tom: Iss. 9
• Strony: 5--16
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr., wzory

Twórcy

autor

Katunin A.

• Institute of Fundamentals of Machinery Design, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland

autor

Zuba M.

• Institute of Fundamentals of Machinery Design, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland

Bibliografia

• [1] West W.M., Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen, Proceedings of the Air Force Conference on Aircraft Structural Integrity, 1-6, 1984.
• [2] Leiven N.A.J., Ewins D.J., Spatial correlation of mode shapes, the Coordinate Modal Assurance Criterion (COMAC), Proceedings of the Sixth International Modal Analysis Conference, 1, 690-695, 1988.
• [3] Shi Z.Y., Law S.S., Zhang L.M., Damage localization by directly using incomplete mode shapes, Journal of Engineering Mechanics, 126(6), 656-660, 2000.
• [4] Ismail Z., Abdul Razak H., Abdul Rahman A.G., Determination of damage location in RC beams using mode shape derivatives, Engineering Structures, 28(11), 1566-1573, 2006.
• [5] Whalen T.M., The behavior of higher order mode shape derivatives in damaged, beam-like structures, Journal of Sound and Vibration, 309(3-5), 426-464, 2008.
• [6] Douka E., Loutridis S., Trochidis A., Crack identification in beams using wavelet analysis, International Journal of Solids and Structures, 40(13-14), 3557-3569, 2003.
• [7] Rucka M., Wilde K., Application of continuous wavelet transform in vibration based damage detection method for beams and plates, Journal of Sound and Vibration, 297(3-5), 536-550, 2006.
• [8] Zhong S., Oyadiji S.O., Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform, Mechanical Systems and Signal Processing, 21(4), 1853-1884, 2007.
• [9] Katunin A., Holewik F., Crack identification in composite elements with non-linear geometry using spatial wavelet transform, Archives of Civil and Mechanical Engineering, 13(3), 287-296, 2013.
• [10] Katunin A., Przystałka P., Detection and localization of delaminations in composite beams using fractional B-spline wavelets with optimized parameters, Eksploatacja i Niezawodnosc – Maintenance and Reliability, 15(3), 391-399, 2014.
• [11] Simard P., le Tavernier E., Fractal approach for signal processing and application to the diagnosis of cavitation, Mechanical Systems and Signal Processing, 14(3), 459-469, 2000.
• [12] Purintrapiban U., Kachitvichyanukul V., Detecting patterns in process data with fractal dimension, Computers & Industrial Engineering, 45(4), 653-667, 2003.
• [13] Gotoh K., Hayakawa M., Smirnova N.A., Hattori K., Fractal analysis of seismogenic ULF emissions, Physics and Chemistry of the Earth, 29(4-9), 419-424, 2004.
• [14] Solhjoo S., Nasrabadi A.M., Golpayegani M.R.H., EEG-based mental task classification in hypnotized and normal subjects, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, 2041-2043, 2005.
• [15] Mishra A.K., Raghav S., Local fractal dimension based ECG arrhythmia classification, Biomedical Signal Processing and Control, 5(2), 114-123, 2010.
• [16] Hadjileontiadis L.J., Douka E., Trochidis A., Fractal dimension analysis for crack identification in beam structures, Mechanical Systems and Signal Processing, 19(3), 659-674, 2005.
• [17] Wang J., Qiao P., Improved damage detection for beam-type structures using a uniform load surface, Structural Health Monitoring, 6(2), 99-110, 2007.
• [18] Qiao P., Cao M., Waveform fractal dimension for mode shape-based damage identification of beam-type structures, International Journal of Solids and Structures, 45(22-23), 5946-5961, 2008.
• [19] Li H., Huang Y., Ou J., Bao Y., Fractal dimension-based damage detection method for beams with a uniform cross-section, Computer-Aided Civil and Infrastructure Engineering, 26, 190-206, 2011.
• [20] An Y., Ou J., Experimental and numerical studies on damage localization of simply supported beams based on curvature difference probability method of waveform fractal dimension, Journal of Intelligent Material Systems and Structures, 23(4), 415-426, 2011.
• [21] Bai R., Cao M., Su Z., Ostachowicz W., Xu H., Fractal dimension analysis of higher-order mode shapes for damage identification of beam structures, Mathematical Problems in Engineering, 2012, ID 454568, 2012.
• [22] Bai R.B., Song X.G., Radzieński M., Cao M.S., Ostachowicz W., Wang S.S., Crack location in beams by data fusion of fractal dimension features of laser-measured operating deflection shapes, Smart Structures and Systems, 13(6), 975-991, 2014.
• [23] Katunin A., Fractal dimension-based crack identification technique of composite beams for on-line SHM systems, Machine Dynamics Research, 34(2), 60-69, 2010.
• [24] Katunin A., Serzysko K., Detection and localization of cracks in composite beams using fractal dimension-based algorithms – a comparative study, Machine Dynamics Research 38(2), 27-36, 2014.
• [25] Katz M., Fractals and the analysis of waveforms, Computers in Biology and Medicine, 18, 145-156, 1988.
• [26] Higuchi T., Approach to an irregular time series on the basis of the fractal theory, Physica D, 31, 277-283, 1988.
• [27] Petrosian A., Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns, Proceedings of IEEE Symposium on Computer-Based Medical Systems, 212–217, 1995.
• [28] Sevcik C., On fractal dimension of waveforms, Chaos Solitons and Fractals, 28, 579-580, 2006.
• [29] Esteller R., Vachtsevanos G., Echauz J., Litt B., A comparison of fractal dimension algorithms using synthetic and experimental data, Proceedings of the 1999 IEEE International Symposium of Circuits and Systems, 3, 199–202, 1999.
• [30] Raghavendra B.S., Dutt D.N., Computing fractal dimension of signals using multiresolution box-counting method, International Journal of Engineering and Mathematical Sciences, 6, 53–68, 2010.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).