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

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.

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Ming Gu

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.


Author(s):  
Fuli Zhang ◽  
Olga Brezhneva ◽  
Amit Shukla

The optimal sensor placement (OSP) problem is integral to modern large scale structures for their health monitoring. Evolutionary algorithms for the OSP problem are attractive as they can result in global optima without gradient information. In this paper, a modification of the Monkey Algorithm with a chaotic search strategy and adaptive parameters is proposed. It includes chaotic initialization, variable search step length, and adaptive watching time. The performance of the proposed chaotic Monkey Algorithm (cMA) is compared with the original Monkey Algorithm. Convergence property of cMA is established. The proposed method is applied to an optimal sensor placement problem for structural health monitoring. The OSP problem is solved for a mass-spring-damper system and then for a model of the I-40 bridge developed by the Los Alamos National Laboratory. Numerical results demonstrate that the proposed Chaotic Monkey Algorithm has capability of solving mixed-variable optimization problems and that it performs better than the originally proposed Monkey algorithm. Finally, nonparametric uncertainty modeling is used to evaluate variability in a model and its effect on the optimal sensor placement.


2017 ◽  
Vol 140 ◽  
pp. 213-224 ◽  
Author(s):  
Chen Yang ◽  
Xuepan Zhang ◽  
Xiaoqi Huang ◽  
ZhengAi Cheng ◽  
Xinghua Zhang ◽  
...  

2016 ◽  
Vol 07 (06) ◽  
pp. 814-823 ◽  
Author(s):  
U. Muthuraman ◽  
M. M. Sai Hashita ◽  
N. Sakthieswaran ◽  
P. Suresh ◽  
M. Raj Kumar ◽  
...  

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