Coherence-based sensor set expansion for optimal sensor placement in active road noise control

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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Observability and Optimal Sensor Placement

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327911101020093 ◽

2012 ◽

Vol 1 (2) ◽

pp. 93-101

Author(s):

Wei Kang ◽

Liang Xu

Keyword(s):

Sensor Placement ◽

Optimal Sensor Placement

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A Data Driven Method for Optimal Sensor Placement in Multi-Zone Buildings

Energy and Buildings ◽

10.1016/j.enbuild.2021.110956 ◽

2021 ◽

pp. 110956

Author(s):

Gowri Suryanarayana ◽

Javier Arroyo ◽

Lieve Helsen ◽

Jesus Lago

Keyword(s):

Sensor Placement ◽

Data Driven ◽

Optimal Sensor Placement

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Optimal Sensor Placement for Reliable Virtual Sensing Using Modal Expansion and Information Theory

Sensors ◽

10.3390/s21103400 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3400

Author(s):

Tulay Ercan ◽

Costas Papadimitriou

Keyword(s):

Utility Function ◽

Measurement Errors ◽

Structural Model ◽

Information Gain ◽

Sensor Placement ◽

Optimal Sensor Placement ◽

Modal Expansion ◽

Expected Information ◽

Virtual Sensing ◽

Multidimensional Integral

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The framework is developed to handle virtual sensing under output-only vibration measurements. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modeling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

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Robust flow control and optimal sensor placement using deep reinforcement learning

Journal of Fluid Mechanics ◽

10.1017/jfm.2020.1170 ◽

2021 ◽

Vol 913 ◽

Cited By ~ 1

Author(s):

Romain Paris ◽

Samir Beneddine ◽

Julien Dandois

Keyword(s):

Reinforcement Learning ◽

Flow Control ◽

Sensor Placement ◽

Optimal Sensor Placement

Abstract

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

Advances in Structural Engineering ◽

10.1177/1369433220947198 ◽

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.

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