Damage Detection of Space Truss Using Second Order Polynomial Method With BFGS Quasi-Newton Optimization

2010 ◽  
Author(s):  
Chun Nam Wong ◽  
Jingqi Xiong ◽  
Hong-Zhong Huang ◽  
Tianyou Hu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Element Model ◽  
Second Order ◽  
Modular Space ◽  
Polynomial Method ◽  
Structural Variants ◽  
Space Truss ◽  
System Equations

A second order damage detection method is developed by expanding the original eigenparameters using their sensitivity terms with the variations in the structural variants. This vibration-based polynomial method is generated from eigenvalue re-analysis in conjunction with the polynomial algorithm. By incorporating basic forms of the Lagrange factor functions, numerical eigenparameter functions are generalized to multi-variate polynomial interpolated forms. Second order sensitivity terms are computed by differentiating these multi-variate eigenparameter functions with respect to the structural variants. Convergence of different order algorithms are compared using finite element model of a four element cantilever beam structure under various damaged percentage cases. Moreover, finite element model of a four bay modular space truss is established. Damage detections from small to large percentages are carried out through numerical simulations on the space truss. Most of these cases converge efficiently toward the ultimate solutions within 1% termination level. Therefore the BFGS algorithm works well with the nonlinear multi-variate system equations. The algorithm operates robustly with limited number of d.o.f.s in the reduced order model and limited number of vibration modes in the full model.

Deep neural networks–based damage detection using vibration signals of finite element model and real intact state: An evaluation via a lab-scale offshore jacket structure

2020 ◽  
pp. 147592172093261 ◽  
Author(s):  
Zohreh Mousavi ◽  
Sina Varahram ◽  
Mir Mohammad Ettefagh ◽  
Morteza H. Sadeghi ◽  
Seyed Naser Razavi
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Deep Neural Network ◽  
Element Model ◽  
Frequency Data ◽  
Vibration Signals ◽  
Intact State ◽  
The Finite Element Model

Structural health monitoring of mechanical systems is essential to avoid their catastrophic failure. In this article, an effective deep neural network is developed for extracting the damage-sensitive features from frequency data of vibration signals to damage detection of mechanical systems in the presence of the uncertainties such as modeling errors, measurement errors, and environmental noises. For this purpose, the finite element method is used to analyze a mechanical system (finite element model). Then, vibration experiments are carried out on the laboratory-scale model. Vibration signals of real intact system are used to updating the finite element model and minimizing the disparities between the natural frequencies of the finite element model and real system. Some parts of the signals that are not related to the nature of the system are removed using the complete ensemble empirical mode decomposition technique. Frequency domain decomposition method is used to extract frequency data. The proposed deep neural network is trained using frequency data of the finite element model and real intact state and then is tested using frequency data of the real system. The proposed network is designed in two stages, namely, the pre-training classification based on deep auto-encoder and Softmax layer (first stage), and the re-training classification based on backpropagation algorithm for fine tuning of the network (second stage). The proposed method is validated using a lab-scale offshore jacket structure. The results show that the proposed method can learn features from the frequency data and achieve higher accuracy than other comparative methods.

Sign in / Sign up

Export Citation Format

Share Document