Sensor Optimal Placement for Structural Health Monitoring Based on Stabilization Diagram

2013 ◽  
Vol 540 ◽  
pp. 47-54 ◽  
Author(s):  
Chun Li Wu ◽  
Han Bing Liu ◽  
Yan Li
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Energy Criterion ◽  
Optimal Placement ◽  
Mode Shapes ◽  
Diagram Method ◽  
Structural Health ◽  
Fitness Functions ◽  
Stabilization Diagram

A novel stabilization diagram method was presented for sensor placement in structural health monitoring of bridges. The aim of the method is to select the optimal locations which can achieve the best identification of modal frequencies and mode shapes. A single parents genetic algorithm was adopted to optimize the sensor locations from a set of coordinate positions. Five fitness functions taken as the objective function are proposed based on effective independence, modal assurance and modal energy criterion, in which the combined fitness functions can obtain more comprehensive properties to reduce the noise interference. The proposed method puts forward a universal way for sensor placement of the civil engineering structure. The effectiveness of the method was proved by a simply supported beam and a continuous beam bridge in the An Longquan interchange overpass.

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.

Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm

2017 ◽  
Vol 17 (2) ◽  
pp. 169-184 ◽  
Author(s):  
Shuo Feng ◽  
Jinqing Jia
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Optimal Placement ◽  
Vibration Test ◽  
Acceleration Sensor ◽  
Optimal Sensor Placement ◽  
Structural Health ◽  
Acceleration Sensors ◽  
Sensor Configuration

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.

On Optimal Sensor Placement Criterion for Structural Health Monitoring with Representative Least Squares Method

2009 ◽  
Vol 413-414 ◽  
pp. 383-391 ◽  
Author(s):  
Dong Sheng Li ◽  
Hong Nan Li ◽  
Claus Peter Fritzen
Keyword(s):  
Structural Health Monitoring ◽  
Least Squares ◽  
Health Monitoring ◽  
Least Squares Method ◽  
Sensor Placement ◽  
Mode Shapes ◽  
Unbiased Estimation ◽  
Matrix Perturbation ◽  
Structural Health ◽  
Vibration Responses

A novel sensor placement criterion is proposed for structural health monitoring after five influencing criteria are critically reviewed. The objective of the proposed criterion is to achieve best identification of modal frequencies and mode shapes through almost unbiased estimation of modal coordinates. The proposed criterion derived by the Representative Least Squares method depends on both the characteristics and the actual loading situations of a structure. It selects sensor positions with the best subspace approximation of the vibration responses from the linear space spanned by the mode shapes. Furthermore, the connection between the Effective Independence and the approximate Representative Least Squares estimator is obtained through matrix perturbation analysis. It is found that the Effective Independence is a step-by-step approximation to that of the Representative Least Squares criterion.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

scholarly journals The role of dynamic response parameters in damage prediction

2019 ◽  
Vol 233 (13) ◽  
pp. 4620-4636 ◽  
Author(s):  
Behzad Ahmed Zai ◽  
MA Khan ◽  
Kamran A Khan ◽  
Asif Mansoor ◽  
Aqueel Shah ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Structural Damage ◽  
Mode Shapes ◽  
Future Research ◽  
Modal Parameter ◽  
Structural Health ◽  
Structural Applications

This article presents a literature review of published methods for damage identification and prediction in mechanical structures. It discusses ways which can identify and predict structural damage from dynamic response parameters such as natural frequencies, mode shapes, and vibration amplitudes. There are many structural applications in which dynamic loads are coupled with thermal loads. Hence, a review on those methods, which have discussed structural damage under coupled loads, is also presented. Structural health monitoring with other techniques such as elastic wave propagation, wavelet transform, modal parameter, and artificial intelligence are also discussed. The published research is critically analyzed and the role of dynamic response parameters in structural health monitoring is discussed. The conclusion highlights the research gaps and future research direction.

scholarly journals Analysis of Guided Wave Propagation in a Multi-Layered Structure in View of Structural Health Monitoring

2019 ◽  
Vol 9 (21) ◽  
pp. 4600 ◽  
Author(s):  
Yevgeniya Lugovtsova ◽  
Jannis Bulling ◽  
Christian Boller ◽  
Jens Prager
Keyword(s):  
Wave Propagation ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Guided Waves ◽  
Oil And Gas ◽  
Numerical Approach ◽  
Guided Wave ◽  
Mode Shapes ◽  
Engineering Structures ◽  
Structural Health

Guided waves (GW) are of great interest for non-destructive testing (NDT) and structural health monitoring (SHM) of engineering structures such as for oil and gas pipelines, rails, aircraft components, adhesive bonds and possibly much more. Development of a technique based on GWs requires careful understanding obtained through modelling and analysis of wave propagation and mode-damage interaction due to the dispersion and multimodal character of GWs. The Scaled Boundary Finite Element Method (SBFEM) is a suitable numerical approach for this purpose allowing calculation of dispersion curves, mode shapes and GW propagation analysis. In this article, the SBFEM is used to analyse wave propagation in a plate consisting of an isotropic aluminium layer bonded as a hybrid to an anisotropic carbon fibre reinforced plastics layer. This hybrid composite corresponds to one of those considered in a Type III composite pressure vessel used for storing gases, e.g., hydrogen in automotive and aerospace applications. The results show that most of the wave energy can be concentrated in a certain layer depending on the mode used, and by that damage present in this layer can be detected. The results obtained help to understand the wave propagation in multi-layered structures and are important for further development of NDT and SHM for engineering structures consisting of multiple layers.

scholarly journals SHMnet: Condition assessment of bolted connection with beyond human-level performance

2019 ◽  
Vol 19 (4) ◽  
pp. 1188-1201 ◽  
Author(s):  
Tong Zhang ◽  
Suryakanta Biswal ◽  
Ying Wang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Steel Frame ◽  
Mode Shapes ◽  
Repeated Impact ◽  
Traditional Methods ◽  
Bolted Connection ◽  
Structural Health ◽  
Sensor Locations

Deep learning algorithms are transforming a variety of research areas with accuracy levels that the traditional methods cannot compete with. Recently, increasingly more research efforts have been put into the structural health monitoring domain. In this work, we propose a new deep convolutional neural network, namely SHMnet, for a challenging structural condition identification case, that is, steel frame with bolted connection damage. We perform systematic studies on the optimisation of network architecture and the preparation of the training data. In the laboratory, repeated impact hammer tests are conducted on a steel frame with different bolted connection damage scenarios, as small as one bolt loosened. The time-domain monitoring data from a single accelerometer are used for training. We conduct parametric studies on different layer numbers, different sensor locations, the quantity of the training datasets and noise levels. The results show that the proposed SHMnet is effective and reliable with at least four independent training datasets and by avoiding vibration node points as sensor locations. Under up to 60% additive Gaussian noise, the average identification accuracy is over 98%. In comparison, the traditional methods based on the identified modal parameters inevitably fail due to the unnoticeable changes of identified natural frequencies and mode shapes. The results provide confidence in using the developed method as an effective structural condition identification framework. It has the potential to transform the structural health monitoring practice. The code and relevant information can be found at https://github.com/capepoint/SHMnet .

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