scholarly journals Optimal Sensor Placement for Inverse Finite Element Reconstruction of Three-Dimensional Frame Deformation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Zhao ◽  
Jingli Du ◽  
Hong Bao ◽  
Qian Xu
Keyword(s):  
Finite Element ◽  
A Priori ◽  
Sensor Placement ◽  
Three Dimensional ◽  
Surface Strain ◽  
Frame Structure ◽  
Optimal Placement ◽  
Optimal Model ◽  
Optimal Sensor Placement ◽  
Modal Shape

The inverse finite element method (iFEM) for the 3D framework deformation reconstruction was introduced. As the process of iFEM did not require a priori knowledge, such as the modal shape, the loading, and the elastic-inertial material information of the structure, it presented high potential in the framework deformation reconstruction. With the current research, it was observed that the key step in the deformation reconstruction of the frame structure with iFEM was the section strains computing of the beam element from the surface strain measurements. The corresponding stability was severely affected by the placement of strain sensors. Therefore, it was necessary to discover a suitable sensor placement to maintain the stability of section strains computing. For this problem, one optimal model of sensor placement was proposed in this paper. Firstly, the well-separated eigenvalues were applied as the optimization target to construct the optimal model. Following, an optimal sensor placement was obtained through the optimal placement model solution, with the particle swarm optimization (PSO) method. Finally, the effectiveness of optimal placement was verified though the accuracy comparison of iFEM deformation reconstruction of a wing-like frame subjected to various loads for different schemes of sensor placement.

Experimental investigation on multi-objective multi-type sensor optimal placement for structural damage detection

2018 ◽  
Vol 18 (3) ◽  
pp. 882-901 ◽  
Author(s):  
Jian-Fu Lin ◽  
You-Lin Xu ◽  
Sheng Zhan
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Structural Damage ◽  
Sensor Placement ◽  
Element Model ◽  
Frame Structure ◽  
Optimal Placement ◽  
Optimal Sensor Placement ◽  
Damage Scenarios ◽  
The Finite Element Model

An optimal sensor placement with multiple types of sensors could provide informative data of a structure to facilitate its structural damage detection. A response covariance-based multi-objective multi-type sensor optimal placement method has been thus developed. To validate this method, an experimental investigation was designed and performed in terms of a nine-bay three-dimensional frame structure, and the experimental details and results are presented in this article. The frame structure was first built, and a finite element model of the frame structure was constructed and updated. The proposed method was then applied to the finite element model to find the optimal sensor placement configuration. The multi-type sensors were then installed on the frame structure according to the determined optimal sensor numbers and positions. Different damage scenarios were then generated on the frame structure. These damage scenarios covered single and multiple damage cases occurring at different locations with different damage severities. A series of experiments, including the optimal and non-optimal sensor placements, were finally carried out, and the measurement data were used together with the finite element model to identify damage quantitatively. The identification results show that the optimal multi-type sensor placement determined by the proposed method could provide accurate damage localization and satisfactory damage quantitation and that the optimal sensor placement yielded better damage identification than the non-optimal sensor placement.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

scholarly journals A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring

2020 ◽  
Vol 10 (21) ◽  
pp. 7710
Author(s):  
Tsung-Yueh Lin ◽  
Jin Tao ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Evolutionary Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Frame Structure ◽  
Mode Shapes ◽  
Beam Model ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Structural Health

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

Determination and Comparison of Optimal Placement Scheme for Dalian Internatioanl Trade Mansion

2011 ◽  
Vol 243-249 ◽  
pp. 5219-5222 ◽  
Author(s):  
Ting Hua Yi ◽  
Hong Nan Li ◽  
Ming Gu
Keyword(s):  
Dynamic Behavior ◽  
Sensor Placement ◽  
Flexible Structures ◽  
Vibration Characteristics ◽  
Tall Building ◽  
The State ◽  
Optimal Placement ◽  
Optimal Sensor Placement ◽  
Structural Dynamic ◽  
Calculation Results

This paper considers the problem of locating sensors on the flexible structures with the aim of maximizing the data information so that structural dynamic behavior could be fully characterized. Since only translational degrees of freedoms (DOFs) are considered for possible sensor installation in the state-of-the-practice, a method which could avoid distinguishing the translational and rotational DOFs is presented. In order to realize the proposed method and to demonstrate its effectiveness, three different sensor placement techniques, EfI, EfI-DPR and Uniform, were employed to a super tall building. The calculation results showed that the proposed method is easy in understanding and could be used practically by civil engineers. EfI method provides a more effective method for optimal sensor placement than other ones to identify the vibration characteristics of the studied building.

scholarly journals Optimal Sensor Placement for Health Monitoring of High-Rise Structure Based on Genetic Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement ◽  
Selection Scheme ◽  
Dual Structure ◽  
Coding Method

Optimal sensor placement (OSP) technique plays a key role in the structural health monitoring (SHM) of large-scale structures. Based on the criterion of the OSP for the modal test, an improved genetic algorithm, called “generalized genetic algorithm (GGA)”, is adopted to find the optimal placement of sensors. The dual-structure coding method instead of binary coding method is proposed to code the solution. Accordingly, the dual-structure coding-based selection scheme, crossover strategy and mutation mechanism are given in detail. The tallest building in the north of China is implemented to demonstrate the feasibility and effectiveness of the GGA. The sensor placements obtained by the GGA are compared with those by exiting genetic algorithm, which shows that the GGA can improve the convergence of the algorithm and get the better placement scheme.

scholarly journals Modelling and Structural Analysis of Three-Dimensional Wing

2019 ◽  
Vol 9 (1) ◽  
pp. 6820-6828
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Structural Analysis ◽  
Elastic Strain ◽  
Structural Investigation ◽  
Three Dimensional ◽  
Structural Behavior ◽  
Natural Rate ◽  
Modal Shape ◽  
Element Analysis

This paper attempts to discover the structural behavior of the wing imperiled to flowing loads through the voyage. The study uses a method in the form of finite element analysis of wing flexure distortion. As a first step, two wing models are established by captivating factual features, wing assembly, and plan principles into consideration. The gathering wing prototypical entails of tinny membrane, two poles, and multi-ribs. Two spars which consist of primary and secondary spars. NACA 23015 is chosen as the baseline aerofoil as this is identical alike to the tailored aerofoil being castoff in Airbus A320. Two rods mostly endure the twisting moment and trim strength, which is finished of titanium contaminant to ensure enough inflexibility. The covering and wing spars are made of aluminum amalgam to lessen the structural heaviness. Later, a static structural investigation is smeared, and the overall distortion, comparable elastic strain, and corresponding VonMises tension are obtained to analyze the mechanical behavior of the wing. Furthermore, modal investigation is being supported out to determine the natural rate of recurrence, as well as the modal shape of the three orders, which are acquired through the pre-stress modal analysis. The outcomes of the modal scrutiny aid engineers decrease excitation on the natural occurrences and avert the wing from the flurry. In view of the results obtained from the study, designers can emphasize consolidation and analysis the stress attentiveness range and huge distortion area. In conclusion, the recreation consequences indicate that the arrangement is possible and improves the information grade of the lifting surface.

Impact of Accelerometer Placement on Modal Extraction of Offshore Wind Structures

2020 ◽  
Author(s):  
M. Richmond ◽  
S. Siedler ◽  
M. Häckell ◽  
U. Smolka ◽  
A. Kolios
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Sensor Placement ◽  
Element Model ◽  
Offshore Wind ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Optimal Sensor Placement ◽  
Permanent Campaign

Abstract The modal parameters extracted from a structure by accelerometers can be used for damage assessment as well as model updating. To extract modal parameters from a structure, it is important to place accelerometers at locations with high modal displacements. Sensor placement can be restricted by practical considerations, and installation might be conducted more based on engineering judgement rather than analysis. This leads to the question of how important the optimal sensor placement is, and if fewer sensors suffice to extract the modal parameters. In this work, an offshore wind substation (OSS) from the Wikinger offshore wind farm (owned by Iberdrola) is instrumented with 12, 3-axis accelerometers. This sensor setup consists of 6 sensors in a permanent campaign where sensors were placed based purely on engineering judgement, as well as 6 sensors in a temporary campaign, placed based on a placement analysis. An optimal sensor placement study was conducted using a finite element model of the structure in the software package FEMtools, resulting in optimal layouts. The temporary campaign sensors were placed such that they, in combination with the permanent campaign, can be used to complete the proposed layouts. Samples for each setup are processed using the software ARTeMIS modal to extract the mode shapes and natural frequencies through the Stochastic Subspace Identification (SSI) technique. The frequencies found by this approach are then clustered together using a k-means algorithm for a comparison within clusters. The modal assurance criterion (MAC) values are calculated for each result and compared to the finite element model from which the optimal sensor placement study was conducted. This is to match mode shapes between the two and thus determine the importance of off diagonal MAC elements in the sensor optimization process. MAC values are also calculated relative to a cluster-averaged set of eigenvectors to determine how they vary over the 1.5 months. The results show that for all sensor layouts, the three lower frequency modes are consistently identified. The most optimized sensor layout, consisting of only 3 sensors, was able to distinguish an additional, higher frequency mode which was never identified in the 6-sensor permanent layout. However, the reduced sensor layout shows slightly more scatter in the results than the 6-sensor layout. There is a higher signal to noise ratio in the temporary campaign which results in scatter. We conclude that with an optimized placement of accelerometers, more modes can be identified and distinguished. However, off diagonal elements in the original MAC matrix, as well as loss of sensor degrees of freedom, can result in additional scatter in the measurements. Some of these findings can be extended to other offshore jacket structures, such as those of wind turbines, in that it gives a better understanding of the consequence of an optimal sensor placement study.

Optimal sensor placement based on multiattribute decision-making considering the common cause failure

2019 ◽  
Vol 233 (19-20) ◽  
pp. 7170-7182 ◽  
Author(s):  
Tao Feng ◽  
Rongxing Duan ◽  
Yanni Lin ◽  
Yining Zeng
Keyword(s):  
Decision Making ◽  
Sensor Placement ◽  
Fault Tree ◽  
Optimal Placement ◽  
Decision Matrix ◽  
Optimal Sensor Placement ◽  
Common Cause ◽  
Common Cause Failure ◽  
Dynamic Fault Tree ◽  
The Common

A new optimal sensor placement is developed to improve the efficiency of fault diagnosis based on multiattribute decision-making considering the common cause failure. The optimal placement scheme is selected based on the reliability of the top event on condition that the number of sensors is preset. Specifically, a β-factor model is introduced to deal with the common cause failure, and dynamic fault tree is used to describe the dynamic failure behaviors. Besides, a dynamic fault tree is converted into a dynamic Bayesian network to calculate the reliability parameters, which construct the decision matrix. Furthermore, an efficient TOPSIS algorithm is adopted to determine the potential locations of sensors. In addition, a diagnostic sensor model is developed to take into account the failure sequence between a sensor and a component using a priority AND gate, and the failure probability of the top event for all sensor placement scenarios is calculated to determine the optimal sensor placement. Finally, a case is provided to prove that the common cause failure has made a considerable impact on the sensor placement.

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