Adaptive-scale damage detection strategy for plate structures based on wavelet finite element model

2015 ◽  
Vol 54 (2) ◽  
pp. 239-256 ◽  
Author(s):  
Wen-Yu He ◽  
Songye Zhu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Detection ◽  
Element Model ◽  
Plate Structures ◽  
Detection Strategy ◽  
Wavelet Finite Element

A Multi-Scale Wavelet Finite Element Model for Damage Detection of Beams Under a Moving Load

2018 ◽  
Vol 18 (06) ◽  
pp. 1850078 ◽  
Author(s):  
Wen-Yu He ◽  
Songye Zhu ◽  
Zhi-Wei Chen
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Dynamic Analysis ◽  
Damage Detection ◽  
Model Updating ◽  
Moving Load ◽  
Element Model ◽  
Multi Scale ◽  
Computation Efficiency ◽  
Wavelet Finite Element

The resolution of structural finite element model (FEM) determines the computation cost and accuracy in dynamic analysis. This study proposes a novel wavelet finite element model (WFEM), which facilitates adaptive mesh refinement, for the dynamic analysis and damage detection of beam structures subjected to a moving load (ML). The multi-scale equations of motion for the beam under the ML are derived using the second-generation cubic Hermite multi-wavelets as the shape functions. Then an adaptive-scale analysis strategy is established, in which the scales of the wavelet beam elements are dynamically changed according to the ML position. The performance of the multi-scale WFEM is examined in both dynamic analysis and damage detection problems. It is demonstrated that the multi-scale WFEM with a similar number of degrees of freedom can achieve much higher accuracy than the traditional FEM. In particular, the multi-scale WFEM enables the detection of sub-element damage with a progressive model updating process. The advantage in computation efficiency and accuracy makes the proposed method a promising tool for multi-scale dynamic analysis or damage detection of structures.

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