Optimal sensor placement for deployable antenna module health monitoring in SSPS using genetic algorithm

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

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

Mathematical Problems in Engineering ◽

10.1155/2011/395101 ◽

2011 ◽

Vol 2011 ◽

pp. 1-12 ◽

Cited By ~ 43

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.

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An Approach for Damage Identification and Optimal Sensor Placement in Structural Health Monitoring by Genetic Algorithm Technique

Circuits and Systems ◽

10.4236/cs.2016.76070 ◽

2016 ◽

Vol 07 (06) ◽

pp. 814-823 ◽

Cited By ~ 5

Author(s):

U. Muthuraman ◽

M. M. Sai Hashita ◽

N. Sakthieswaran ◽

P. Suresh ◽

M. Raj Kumar ◽

...

Keyword(s):

Genetic Algorithm ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Damage Identification ◽

Sensor Placement ◽

Optimal Sensor Placement ◽

Structural Health

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A New Optimal Sensor Placement Strategy Based on Modified Modal Assurance Criterion and Improved Adaptive Genetic Algorithm for Structural Health Monitoring

Mathematical Problems in Engineering ◽

10.1155/2015/626342 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Can He ◽

Jianchun Xing ◽

Juelong Li ◽

Qiliang Yang ◽

Ronghao Wang ◽

...

Keyword(s):

Genetic Algorithm ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Sensor Placement ◽

Adaptive Genetic Algorithm ◽

Optimal Sensor Placement ◽

Modal Assurance Criterion ◽

Modal Strain Energy ◽

Structural Health ◽

Computation Efficiency

Optimal sensor placement (OSP) is an important part in the structural health monitoring. Due to the ability of ensuring the linear independence of the tested modal vectors, the minimum modal assurance criterion (minMAC) is considered as an effective method and is used widely. However, some defects are present in this method, such as the low modal energy and the long computation time. A new OSP method named IAGA-MMAC is presented in this study to settle the issue. First, a modified modal assurance criterion (MMAC) is proposed to improve the modal energy of the selected locations. Then, an improved adaptive genetic algorithm (IAGA), which uses the root mean square of off-diagonal elements in the MMAC matrix as the fitness function, is proposed to enhance computation efficiency. A case study of sensor placement on a numerically simulated wharf structure is provided to verify the effectiveness of the IAGA-MMAC strategy, and two different methods are used as contrast experiments. A comparison of these strategies shows that the optimal results obtained by the IAGA-MMAC method have a high modal strain energy, a quick computational speed, and small off-diagonal elements in the MMAC matrix.

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A novel genetic algorithm with CDF5/3 filter-based lifting scheme for optimal sensor placement

International Journal of Innovative Computing and Applications ◽

10.1504/ijica.2021.10036512 ◽

2021 ◽

Vol 12 (2/3) ◽

pp. 67

Author(s):

Amandeep Singh Bhatia ◽

Soumya Ranjan Nayak ◽

T. Ganesan ◽

Pothuraju Rajarajeswari

Keyword(s):

Genetic Algorithm ◽

Sensor Placement ◽

Lifting Scheme ◽

Optimal Sensor Placement

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

Applied Sciences ◽

10.3390/app10217710 ◽

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.

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Near-optimal sensor placement for health monitoring of civil structures

10.1117/12.847704 ◽

2010 ◽

Cited By ~ 1

Author(s):

Gwendolyn W. van der Linden ◽

Abbas Emami-Naeini ◽

Robert L. Kosut ◽

Hassan Sederat ◽

Jerome P. Lynch

Keyword(s):

Health Monitoring ◽

Sensor Placement ◽

Civil Structures ◽

Optimal Sensor Placement

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Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

Structural Health Monitoring ◽

10.1177/14759217211056557 ◽

2021 ◽

pp. 147592172110565

Author(s):

Chungeon Kim ◽

Hyunseok Oh ◽

Byung Chang Jung ◽

Seok Jun Moon

Keyword(s):

Genetic Algorithm ◽

Simulation Model ◽

Sensor Network ◽

Sensor Placement ◽

Mixed Integer ◽

Working Fluid ◽

Small Scale ◽

Pipeline System ◽

Optimal Sensor Placement ◽

Pipeline Network

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

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