A novel formulation for finite element model-based damage detection

2014 ◽  
pp. 2003-2007 ◽  
Author(s):  
Z Wu ◽  
G Xu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Element Model ◽  
Model Based

scholarly journals A Kriging Model Based Finite Element Model Updating Method for Damage Detection

2017 ◽  
Vol 7 (10) ◽  
pp. 1039 ◽  
Author(s):  
Xiuming Yang ◽  
Xinglin Guo ◽  
Huajiang Ouyang ◽  
Dongsheng Li
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Model Updating ◽  
Element Model ◽  
Kriging Model ◽  
Finite Element Model Updating ◽  
Model Based

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.

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