An Improved Genetic Algorithm for Optimal Sensor Placement in Space Structures Damage Detection

2014 ◽  
Vol 29 (3) ◽  
pp. 121-136 ◽  
Author(s):  
Sahar Beygzadeh ◽  
Eysa Salajegheh ◽  
Peyman Torkzadeh ◽  
Javad Salajegheh ◽  
Seyed Sadegh Naseralavi
Keyword(s):  
Genetic Algorithm ◽  
Damage Detection ◽  
Sensor Placement ◽  
Space Structures ◽  
Improved Genetic Algorithm ◽  
Optimal Sensor Placement

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.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

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.

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

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

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

2017 ◽  
Vol 140 ◽  
pp. 213-224 ◽  
Author(s):  
Chen Yang ◽  
Xuepan Zhang ◽  
Xiaoqi Huang ◽  
ZhengAi Cheng ◽  
Xinghua Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Optimal Sensor Placement ◽  
Antenna Module

Optimal Sensor Placement for Damage Detection in Complex Structures

2009 ◽  
Author(s):  
Sunilkumar Soni ◽  
Santanu Das ◽  
Aditi Chattopadhyay
Keyword(s):  
False Alarm ◽  
Damage Detection ◽  
False Alarm Rate ◽  
Structural Component ◽  
Detection Rate ◽  
Sensor Placement ◽  
False Alarms ◽  
Optimal Sensor Placement ◽  
Detection Scheme ◽  
Sensing Radius

An optimal sensor placement methodology is proposed based on detection theory framework to maximize the detection rate and minimize the false alarm rate. Minimizing the false alarm rate for a given detection rate plays an important role in improving the efficiency of a Structural Health Monitoring (SHM) system as it reduces the number of false alarms. The placement technique is such that the sensor features are as directly correlated and as sensitive to damage as possible. The technique accounts for a number of factors, like actuation frequency and strength, minimum damage size, damage detection scheme, material damping, signal to noise ratio (SNR) and sensing radius. These factors are not independent and affect each other. Optimal sensor placement is done in two steps. First, a sensing radius, which can capture any detectable change caused by a perturbation and above a certain threshold, is calculated. This threshold value is based on Neyman-Pearson detector that maximizes the detection rate for a fixed false alarm rate. To avoid sensor redundancy, a criterion to minimize sensing region overlaps of neighboring sensors is defined. Based on the sensing region and the minimum overlap concept, number of sensors needed on a structural component is calculated. In the second step, a damage distribution pattern, known as probability of failure distribute, is calculated for a structural component using finite element analysis. This failure distribution helps in selecting the most sensitive sensors, thereby removing those making remote contributions to the overall detection scheme.

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

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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