Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system

Mojtahedi, Alireza; Lotfollahi-Yaghin, Mohammad Ali; Hassanzadeh, Yousef; Ettefagh, Mir Mohammad; Aminfar, Mohammad H.; Aghdam, A.B.

doi:10.1016/j.apor.2011.05.001

Cited by 55 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any major structural damage to the platform is refl ected by a change in structural response. 18 The method is known to be sensitive enough to detect a frequency change of 0.5%. However, the method cannot detect minor damage, such as small defects or local fatigue cracks.…”

Section: Vibration-based Damage Assessment Approachesmentioning

confidence: 99%

Sensing solutions for assessing and monitoring offshore structures

Kim

Lee

2014

Sensor Technologies for Civil Infrastructures

View full text Add to dashboard Cite

Section: Vibration-based Damage Assessment Approachesmentioning

confidence: 99%

Sensing solutions for assessing and monitoring offshore structures

Kim

Lee

2014

Sensor Technologies for Civil Infrastructures

View full text Add to dashboard Cite

“…Investigating the literature indicates that the effectiveness of the presented methods for damage detection have usually been evaluated using the white Gaussian noise and impact excitations which are produced by a shaker and hammer, respectively. 14,[18][19][20][21] From this standpoint, in the experimental tests of this study, a shaker is used to produce artificial excitation, and the force generated is white Gaussian noise in the desired frequency range, which contains all frequencies within the desired frequency range. The random excitation force is shown in Figure 5.…”

Section: Experimental Setup (Lab-scale Offshore Jacket Model)mentioning

confidence: 99%

“…The FE model is updated using a standard optimization algorithm to solve a constrained problem. The objective function to be minimized can be expressed as follows 14,15,35,36…”

Section: Fe Model Updating and Preprocessingmentioning

confidence: 99%

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

Mousavi

Varahram

Ettefagh

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

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.

show abstract

“…An estimation of corrosion damages in bridges is investigated in Jang et al 19 based on eigenvector residuals. In Morassi and Rovere, 20 damage localization is investigated using model updating methods and frequency measurements at a frame structure, whereas in Mojtahedi et al 21 the authors successfully try to perform model updating for damage localization in a jacket structure, based on eigenvector and frequency residuals.…”

Section: Introductionmentioning

confidence: 99%

A two-step approach to damage localization at supporting structures of offshore wind turbines

Gebhardt

Rolfes

2017

Structural Health Monitoring

View full text Add to dashboard Cite

This article introduces a new adaptive two-step optimization algorithm for finite element model updating with special emphasis on damage localization at supporting structures of offshore wind turbines. The algorithm comprises an enhanced version of the global optimization algorithm simulated annealing, the simulated quenching method that approximates an initial guess of damage localization. Subsequently, sequential quadratic programming is used to compute the final solution adaptively. For the correlation of numerical model and measurement data, both a measure based on eigenfrequencies and mode shapes and a measure employing time series are implemented and compared with respect to their performance for damage localization. Phase balance of the time signals is achieved using cross-correlation. The localization problem is stated as a minimization problem in which the measures are used in time and modal domain as the objective function subject to constraints. Furthermore, the objective function value of the adjusted model is used to distinguish correct from wrong solutions. The functionality is proven using a numerical model of a monopile structure with simulated damage and a lab-scaled model of a tripile structure with real damage.

show abstract

Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system

Cited by 55 publications

References 30 publications

Sensing solutions for assessing and monitoring offshore structures

Sensing solutions for assessing and monitoring offshore 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

A two-step approach to damage localization at supporting structures of offshore wind turbines

Contact Info

Product

Resources

About