Machine-learning-based damage identification methods with features derived from moving principal component analysis

Reference

• Y. An, B. F. Spencer, and J. Ou, “A test method for damage diagnosis of suspension bridge suspender cables,” Comput-Aided. Civil Infrastruct. Eng., vol. 30, no. 10, pp. 771–784, 2015. DOI: 10.1111/mice.12144.
   Web of Science ®Google Scholar
• X. Xu, R. Augello, and H. Yang, “The generation and validation of a CUF-based FEA model with laser-based experiments,” Mech. Adv. Mater. Struct., pp. 1–8, 2019. DOI: 10.1080/15376494.2019.1697473.
   Web of Science ®Google Scholar
• B. Gunes and O. Gunes, “Structural health monitoring and damage assessment Part I: a critical review of approaches and methods,” Int. J. Phys. Sci., vol. 8, no. 34, pp. 1694–1702, 2013. DOI: 10.5897/IJPS11.1327.
   Google Scholar
• Z. S. Jiang, Z. Y. Zhang, and A. Maxwell, “Extraction of structural modal information using acoustic sensor measurements and machine learning,” J. Sound Vib., vol. 450, pp. 156–174, 2019. DOI: 10.1016/j.jsv.2019.03.009.
   Web of Science ®Google Scholar
• X. Xu, J. Bureick, H. Yang, and I. Neumann, “TLS-based composite structure deformation analysis validated with laser tracker,” Compos. Struct., vol. 202, pp. 60–65, 2018. DOI: 10.1016/j.compstruct.2017.10.015.
   Web of Science ®Google Scholar
• X. Xu, H. Yang, R. Augello, and E. Carrera, “Optimized free-form surface modeling of point clouds from laser-based measurement,” Mech. Adv. Mater. Struct, pp. 1–9, 2019. DOI: 10.1080/15376494.2019.1688435.
   Web of Science ®Google Scholar
• Y.-z. Lin, Z.-h. Nie, and H.-w. Ma, “Structural damage detection with automatic feature-extraction through deep learning,” Comput-Aided. Civil Infrastruct. Eng., vol. 32, no. 12, pp. 1025–1046, 2017. DOI: 10.1111/mice.12313.
   Web of Science ®Google Scholar
• J. Amezquita-Sanchez and H. Adeli, “Feature extraction and classification techniques for health monitoring of structures,” Sci. Iran. A, vol. 22, no. 6, pp. 1931, 2015. DOI: 10.1007/s11831-014-9135-7.
   Web of Science ®Google Scholar
• H. Yang and X. Xu, “Multi-sensor technology for B-spline modelling and deformation analysis of composite structures,” Compos. Struct., vol. 224, pp. 111000, 2019. DOI: 10.1016/j.compstruct.2019.111000.
   Web of Science ®Google Scholar
• H. Yang, X. Xu, B. Kargoll, and I. Neumann, “An automatic and intelligent optimal surface modeling method for composite tunnel structures,” Compos. Struct., vol. 208, pp. 702–710, 2019. DOI: 10.1016/j.compstruct.2018.09.082.
   Web of Science ®Google Scholar
• M. Cao, W. Xu, W. Ostachowicz, and Z. Su, “Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature,” J. Sound Vib., vol. 333, no. 6, pp. 1543–1553, 2014. DOI: 10.1016/j.jsv.2013.11.003.
   Web of Science ®Google Scholar
• J. Xiang, T. Matsumoto, Y. Wang, and Z. Jiang, “Detect damages in conical shells using curvature mode shape and wavelet finite element method,” Int. J. Mech. Sci., vol. 66, pp. 83–93, 2013. DOI: 10.1016/j.ijmecsci.2012.10.010.
   Web of Science ®Google Scholar
• W. Fan and P. Z. Qiao, “Vibration-based damage identification methods: a review and comparative study,” Struct. Health Monit., vol. 10, no. 1, pp. 83–111, 2011. DOI: 10.1177/1475921710365419.
   Web of Science ®Google Scholar
• H. Hu and J. Wang, “Damage detection of a woven fabric composite laminate using a modal strain energy method,” Eng. Struct., vol. 31, no. 5, pp. 1042–1055, 2009. DOI: 10.1016/j.engstruct.2008.12.015.
   Web of Science ®Google Scholar
• V. Shahsavari, L. Chouinard, and J. Bastien, “Wavelet-based analysis of mode shapes for statistical detection and localization of damage in beams using likelihood ratio test,” Eng. Struct., vol. 132, pp. 494–507, 2017. DOI: 10.1016/j.engstruct.2016.11.056.
   Web of Science ®Google Scholar
• F. Shabbir and P. Omenzetter, “Particle swarm optimization with sequential niche technique for dynamic finite element model updating,” Comput-Aided. Civil Infrastruct. Eng., vol. 30, no. 5, pp. 359–375, 2015. DOI: 10.1111/mice.12100.
   Web of Science ®Google Scholar
• D. Posenato, F. Lanata, D. Inaudi, and I. F. C. Smith, “Model-free data interpretation for continuous monitoring of complex structures,” Adv. Eng. Inform., vol. 22, no. 1, pp. 135–144, 2008. DOI: 10.1016/j.aei.2007.02.002.
   Web of Science ®Google Scholar
• I. Laory, T. N. Trinh, D. Posenato, and I. F. C. Smith, “Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures,” J. Comput. Civil. Eng., vol. 27, no. 6, pp. 657–666, 2013. DOI: 10.1061/(ASCE)CP.1943-5487.0000289.
   Web of Science ®Google Scholar
• M. Malekzadeh, M. Gul, I. Kwon, and N. Catbas, “An integrated approach for structural health monitoring using an in-house built fiber optic system and non-parametric data analysis,” Smart Struct. Syst., vol. 14, no. 5, pp. 917–942, 2014. DOI: 10.12989/sss.2014.14.5.917.
   Web of Science ®Google Scholar
• D. Posenato, P. Kripakaran, D. Inaudi, and I. F. C. Smith, “Methodologies for model-free data interpretation of civil engineering structures,” Comput. Struct., vol. 88, no. 7-8, pp. 467–482, 2010. DOI: 10.1016/j.compstruc.2010.01.001.
   Web of Science ®Google Scholar
• G. Zhang, L. Tang, L. Zhou, Z. Liu, Y. Liu, and Z. Jiang, “Principal component analysis method with space and time windows for damage detection,” Sensors (Basel, Switzerland), vol. 19, no. 11, pp. 2521, 2019. DOI: 10.3390/s1911.
   Web of Science ®Google Scholar
• Y. Chien, “Pattern classification and scene analysis,” IEEE Trans. Autom. Control, vol. 19, no. 4, pp. 462–463, 1974. DOI: 10.1109/TAC.1974.1100577.
   Google Scholar
• B. Kilundu, C. Letot, P. Dehombreux, and X. Chiementin, “Early detection of bearing damage by means of decision trees,” J. Autom. Mob. Robot. Intel. Syst., vol. 3, no. 3, pp. 70–74, 2008. DOI: 10.3182/20081205-2-CL-4009.00038.
   Google Scholar
• Q. Zhou, Y. Ning, Q. Zhou, L. Luo, and J. Lei, “Structural damage detection method based on random forests and data fusion,” Struct. Health Monit., vol. 12, no. 1, pp. 48–58, 2013. DOI: 10.1177/1475921712464572.
   Google Scholar
• O. Abdeljaber, S. Sassi, O. Avci, S. Kiranyaz, A. A. Ibrahim, and M. Gabbouj, “Fault detection and severity identification of ball bearings by online condition monitoring,” IEEE Trans. Ind. Electron., vol. 66, no. 10, pp. 8136–8147, 2019. DOI: 10.1109/TIE.2018.2886789.
   Web of Science ®Google Scholar
• C. H. Su et al., “Improved damage localization and quantification of CFRP using lamb waves and convolution neural network,” IEEE Sens. J., vol. 19, no. 14, pp. 5784–5791, 2019. DOI: 10.1109/JSEN.2019.2908838.
   Web of Science ®Google Scholar
• P. G. Asteris, S. Nozhati, M. Nikoo, L. Cavaleri, and M. Nikoo, “Krill herd algorithm-based neural network in structural seismic reliability evaluation,” Mech. Adv. Mater. Struct., vol. 26, no. 13, pp. 1146–1153, 2019. DOI: 10.1080/15376494.2018.1430874.
   Web of Science ®Google Scholar
• L. Tian and Y. Luo, “A comparison study of BPN and SVM prediction models for inherent deformations of T-welded joints,” Mech. Adv. Mater. Struct., pp. 1–19, 2019. DOI: 10.1080/15376494.2019.1567881.
   Web of Science ®Google Scholar
• S. Li, S. Laima, and H. Li, “Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression,” J. Wind. Eng. Ind. Aerod., vol. 172, pp. 196–211, 2018. DOI: 10.1016/j.jweia.2017.10.022.
   Web of Science ®Google Scholar
• V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, “An assessment of the effectiveness of a random forest classifier for land-cover classification,” ISPRS J. Photogramm. Remote Sens., vol. 67, pp. 93–104, 2012. DOI: 10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• G. M. Foody, “Sample size determination for image classification accuracy assessment and comparison,” Int. J. Remote Sens, vol. 30, no. 20, pp. 5273–5291, 2009. DOI: 10.1080/01431160903130937.
   Web of Science ®Google Scholar
• S. Tesfamariam and Z. Liu, “Earthquake induced damage classification for reinforced concrete buildings,” Struct. Saf., vol. 32, no. 2, pp. 154–164, 2010. DOI: 10.1016/j.strusafe.2009.10.002.
   Web of Science ®Google Scholar
• Q. J. Luo, B. Z. Ge, and Q. G. Tian, “A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM,” Constr. Build. Mater., vol. 204, pp. 244–254, 2019. DOI: 10.1016/j.conbuildmat.2019.01.150.
   Web of Science ®Google Scholar
• S. Khan and T. Yairi, “A review on the application of deep learning in system health management,” Mech. Syst. Signal Process., vol. 107, no. 1, pp. 241–265, 2018. DOI: 10.1016/j.ymssp.2017.11.024.
   Web of Science ®Google Scholar
• M. Jayawardhana, X. Zhu, R. Liyanapathirana, and U. Gunawardana, “Statistical damage sensitive feature for structural damage detection using AR model coefficients,” Adv. Struct. Eng., vol. 18, no. 10, pp. 1551–1562, 2015. DOI: 10.1260/1369-4332.18.10.1551.
   Web of Science ®Google Scholar
• K. Wei et al., “Mechanical analysis and modeling of metallic lattice sandwich additively fabricated by selective laser melting,” Thin. Wall. Struct., vol. 146, pp. 106189, 2020. DOI: 10.1016/j.tws.2019.106189.
   Web of Science ®Google Scholar
• T. G. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization,” Mach. Learn., vol. 40, no. 2, pp. 139–157, 2000.
   Web of Science ®Google Scholar
• L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. DOI: 10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• G. E. Batista and M. C. Monard, “A study of K-nearest neighbour as an imputation method,” Hybrid Intell. Syst., vol. 87, pp. 251–260, 2002.
   Google Scholar
• Z. X. Li, B. J. Wu, and Y. Wang, “Statistical analyses on multi-scale features of monitoring data from health monitoring system in long cable supported bridges,” Procedia Eng., vol. 1, no. 1, pp. 123–127, 2009. DOI: 10.1016/j.proeng.2009.06.029.
   Google Scholar
• P. A. Adewuyi, Z. Wu, and S. N. H. M. Kammrujaman, “Assessment of vibration-based damage identification methods using displacement and distributed strain measurements,” Struct. Health Monit., vol. 8, no. 6, pp. 443–461, 2009. DOI: 10.1177/1475921709340964.
   Google Scholar