Nonlinear damage identification of breathing cracks in Truss system

Jie Zhao; Hans DeSmidt

doi:10.1117/12.2045249

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8 March 2014 Nonlinear damage identification of breathing cracks in Truss system

Jie Zhao; Hans DeSmidt

Author Affiliations +

Jie Zhao,¹ Hans DeSmidt¹
¹The Univ. of Tennessee Knoxville (United States)

Proceedings Volume 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014; 90610V (2014) https://doi.org/10.1117/12.2045249
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2014, San Diego, California, United States

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Abstract

The breathing cracks in truss system are detected by Frequency Response Function (FRF) based damage identification method. This method utilizes damage-induced changes of frequency response functions to estimate the severity and location of structural damage. This approach enables the possibility of arbitrary interrogation frequency and multiple inputs/outputs which greatly enrich the dataset for damage identification. The dynamical model of truss system is built using the finite element method and the crack model is based on fracture mechanics. Since the crack is driven by tensional and compressive forces of truss member, only one damage parameter is needed to represent the stiffness reduction of each truss member. Assuming that the crack constantly breathes with the exciting frequency, the linear damage detection algorithm is developed in frequency/time domain using Least Square and Newton Raphson methods. Then, the dynamic response of the truss system with breathing cracks is simulated in the time domain and meanwhile the crack breathing status for each member is determined by the feedback from real-time displacements of member’s nodes. Harmonic Fourier Coefficients (HFCs) of dynamical response are computed by processing the data through convolution and moving average filters. Finally, the results show the effectiveness of linear damage detection algorithm in identifying the nonlinear breathing cracks using different combinations of HFCs and sensors.

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Jie Zhao and Hans DeSmidt "Nonlinear damage identification of breathing cracks in Truss system", Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, 90610V (8 March 2014); doi: 10.1117/12.2045249; https://doi.org/10.1117/12.2045249

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KEYWORDS

Damage detection

Complex systems

Algorithm development

Detection and tracking algorithms

Sensors

Convolution

Data modeling

Finite element methods

Signal processing

Mechanics

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Jie Zhao, Hans DeSmidt, "Nonlinear damage identification of breathing cracks in Truss system," Proc. SPIE 9061, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, 90610V (8 March 2014);

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