A New Damage Index for Structural Health Monitoring: A Comparison of Time and Frequency Domains

Abstract With the adoption of the damage tolerance design principle, the health monitoring system has become an integral part of the operation of engineering structures. For the system to work, a damage indicator that describes the structural integrity level should be established and monitored. The damage indicator is usually derived from structural responses. Many quantities have been proposed for damage indicator including natural frequency, mode shape, curvature, strain energy, and t-, F-, and z-statistics. In this paper, we propose a new damage indicator in the time and frequency domains derived from the Euler-Bernoulli beam theory. We evaluate the method by using data obtained from a numerical simulation of a cracked beam. The beam deformation is nonlinear due to the contact between the crack faces during vibration. The proposed damage index is estimated in the domains for various observation points on the beam. Besides, the existence of the crack is also predicted by the widely used traditional method based on the change of the natural frequency and mode shape. A comparison is made between the present method and the existing ones. We conclude the present proposal is more sensitive to detect the crack.

[1]  Albert C. Esterline,et al.  A Study of Supervised Machine Learning Techniques for Structural Health Monitoring , 2015, MAICS.

[2]  . M.S.Mhaske,et al.  DETECTION OF CRACK LOCATION AND DEPTH IN A CANTILEVER BEAM BY VIBRATION MEASUREMENT AND ITS COMPARATIVE VALIDATION IN ANN AND GA , 2015 .

[3]  Ardeshir Bahreininejad,et al.  Damage detection of truss bridge joints using Artificial Neural Networks , 2008, Expert Syst. Appl..

[4]  G. N. Labeas,et al.  Development and validation of a strain-based Structural Health Monitoring system , 2009 .

[5]  Francesco Archetti,et al.  Smart data driven maintenance: Improving damage detection and assessment on aerospace structures , 2014, 2014 IEEE Metrology for Aerospace (MetroAeroSpace).

[6]  Elsa Caetano,et al.  A vision system for vibration monitoring of civil engineering structures , 2011 .

[7]  Ling Yu,et al.  Structural Damage Detection in a Truss Bridge Model Using Fuzzy Clustering and Measured FRF Data Reduced by Principal Component Projection , 2013 .

[8]  Marcello Vanali,et al.  The measurement network of the san siro meazza stadium in milan: origin and implementation of a new data acquisition strategy for structural health monitoring , 2010 .

[9]  Hong Ye,et al.  Structural Health Monitoring of Bridge Using an Adaptive Real-Time Wireless Sensor Network , 2012 .

[10]  Francesco Archetti,et al.  A Hyper-solution SVM Classification Framework: Application to On-line Aircraft Structural Health Monitoring , 2014 .

[11]  Mohammad Reza Khedmati,et al.  Damage detection by a FE model updating method using power spectral density: Numerical and experimental investigation , 2017 .

[12]  John E. T. Penny,et al.  Crack Modeling for Structural Health Monitoring , 2002 .

[13]  Alessandro De Stefano,et al.  Vibration-based monitoring of civil infrastructure: challenges and successes , 2011 .

[14]  Mehrisadat Makki Alamdari,et al.  A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge , 2017 .

[15]  Dirk Vandepitte,et al.  A comparative assessment of different frequency based damage detection in unidirectional composite plates using MFC sensors , 2016 .

[16]  Jan Drewes Achenbach,et al.  A neural network for crack sizing trained by finite element calculations , 1996 .

[17]  George D. Hatzigeorgiou,et al.  Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis , 2016, Bulletin of Earthquake Engineering.

[18]  F. Gunawan.  THE SENSITIVITY OF THE DAMAGE INDEX OF THE GENERAL VIBRATION METHOD TO DAMAGE LEVEL FOR STRUCTURAL HEALTH MONITORING , 2019 .

[19]  Abhishek Yadav,et al.  pplication of artificial neural network for predicting crack growth irection in multiple cracks geometry , 2015 .

[20]  P. Moser,et al.  Design and deployment of a continuous monitoring system for the dowling hall footbridges , 2013, Experimental Techniques.

[21]  Shinae Jang,et al.  Experimental and numerical evaluation of wavelet based damage detection methodologies , 2015 .

[22]  Pravat Kumar Ray,et al.  Application of Artificial Neural Network for Predicting Fatigue Crack Propagation Life of Aluminum Alloys , 2009 .

[23]  P. K. Ray,et al.  Prediction of mode-I overload-induced fatigue crack growth rates using neuro-fuzzy approach , 2010, Expert Syst. Appl..

[24]  Shih Chieh Lo,et al.  Application of real‐time adaptive identification technique on damage detection and structural health monitoring , 2009 .

[25]  Francesco Archetti,et al.  Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies , 2013 .

[26]  P. K. Ray,et al.  Application of adaptive neuro-fuzzy inference system in modeling fatigue life under interspersed mixed-mode (I and II) spike overload , 2011, Expert Syst. Appl..