A Bayesian approach to functional sensor placement optimization for system health monitoring

Author(s):  
Masoud Pourali ◽  
Ali Mosleh
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.


Author(s):  
Masoud Pourali ◽  
Ali Mosleh

Sensors are being increasingly used for real–time health monitoring of complex systems. The measured quantities are expected to provide real–time information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. The increasing costs of aging systems and infrastructures have become a major concern and real–time health monitoring systems could ensure increased safety and reliability of these systems. Real–time system health monitoring, assesses the state of systems’ health and, through appropriate data processing and interpretation, can predict the remaining life of the system. This paper introduces a method based on Bayesian networks and attempts to find optimum locations of sensors for the best estimate a system health. Information metrics are used for optimized sensor placement based on the value of information that each possible sensor placement scenario provides.


2014 ◽  
Vol 14 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Rohan N. Soman ◽  
Toula Onoufrioua ◽  
Marios A. Kyriakidesb ◽  
Renos A. Votsisc ◽  
Christis Z. Chrysostomou

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