4.7 Article

Optimal sensor placement for strain sensing of a beam of high-speed EMU

Related references

Note: Only part of the references are listed.
Article Mechanics

A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty

Haichao An et al.

Summary: This paper introduces a novel framework for optimizing the number and placement of sensors in vibration-based damage detection in composite structures. The framework incorporates modal effective mass fractions to determine target vibration modes and proposes a modal kinetic-energy-based index to reduce the design space. The optimization problem is solved using the nondominated sorting genetic algorithm II and Monte Carlo simulation.

COMPOSITE STRUCTURES (2022)

Article Engineering, Marine

A new method for optimal sensor placement considering multiple factors and its application to deepwater riser monitoring systems

Yulong Zhang et al.

Summary: A novel multi-factor optimal sensor placement (OSP) strategy, named effective independence-acceleration amplitude-total displacement (EI-AA-TD) method, is proposed in this study, which integrates kinetic energy and strain energy into the classic EI method. The contribution matrixes associated with these two energy indexes are discussed, and a multifactor optimization framework for sensor placement is formulated by integrating energy perspective contribution matrixes and modal observability perspective EI coefficient vector. Additionally, a comprehensive scoring evaluation technique is proposed to assess the quality of sensor layout, and the proposed EI-AA-TD method is validated through a study case of deepwater riser monitoring systems, showing excellent scores and effectiveness of the methods.

OCEAN ENGINEERING (2022)

Article Engineering, Mechanical

On the Bayesian sensor placement for two-stage structural model updating and its validation

Sahil Bansal et al.

Summary: This study proposes an optimal Bayesian sensor placement approach for updating linear structural models, involving two stages of identifying modal parameters and updating model parameters, selecting the sensor configuration that maximizes the expected information gain.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Engineering, Mechanical

Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost

Azin Mehrjoo et al.

Summary: This paper proposes an optimal sensor placement (OSP) framework using information theory, which combines a Bayesian OSP method with modal expansion to minimize information entropy about quantities of interest (QoI) without knowledge of input excitation. The framework takes into account variations in sensor installation cost and has been numerically evaluated using a realistic model of an offshore wind turbine. The results demonstrate that the OSP framework is an effective tool for decision-makers and can offer valuable insights when considering cost constraints.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Engineering, Mechanical

Optimal sensor placement for uncertain inverse problem of structural parameter estimation

Jie Liu et al.

Summary: This paper proposes an optimal sensor placement approach for structural parameter estimation, aiming to alleviate the ill-posedness problem in inverse procedures. The key idea is to select sensor positions with the most sensitive measured responses and the least correlated responses. The optimization strategy converts the traditional minimum variance criterion to a new maximum independent mean-variance criterion, transforming the complex problem into a forward uncertainty propagation problem.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Engineering, Mechanical

A unified sampling-based framework for optimal sensor placement considering parameter and prediction inference

C. Argyris et al.

Summary: A Bayesian framework for optimal sensor placement based on model optimization is presented to minimize uncertainty in predicting a specific quantity of interest. Emphasizing prediction inference over parameter inference, the method aims to reduce uncertainty in key parameters for accurate predictions. By using the determinant to measure uncertainty and evaluating covariance matrices through Monte Carlo sampling, the approach differs from traditional methods and is more suitable for prediction inference.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Engineering, Mechanical

An adaptive sensor placement algorithm for structural health monitoring based on multi-objective iterative optimization using weight factor updating

Chen Yang

Summary: A new sensor placement algorithm based on an iterative updating process is proposed in this study, combining different optimal sensor placement methods using adaptive weight factors and solved by a genetic algorithm.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Chemistry, Analytical

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

Tulay Ercan et al.

Summary: A framework for optimal sensor placement for virtual sensing is proposed based on modal expansion technique and information theory. The framework maximizes a utility function to reduce uncertainty in predicted quantities of interest at virtual sensing locations, considering uncertainties in structural model and modeling error parameters. The Gaussian nature of the response is utilized to derive analytical expressions for the utility function, highlighting the importance of robustness to errors and uncertainties.

SENSORS (2021)

Article Engineering, Multidisciplinary

Optimal sensor placement for spatial lattice structure based on three-dimensional redundancy elimination model

Chen Yang et al.

APPLIED MATHEMATICAL MODELLING (2019)

Article Computer Science, Artificial Intelligence

Optimal sensor placement based on relaxation sequential algorithm

Hong Yin et al.

NEUROCOMPUTING (2019)

Article Engineering, Mechanical

Bayesian virtual sensing in structural

J. Kullaa

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Automation & Control Systems

A Modal Expansion Method for Displacement and Strain Field Reconstruction of a Thin-Wall Component During Machining

Man Yu et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2018)

Article Engineering, Mechanical

Development of a stochastic effective independence (SEFI) method for optimal sensor placement under uncertainty

Taejin Kim et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

Article Computer Science, Interdisciplinary Applications

Sensor placement optimization applied to laminated composite plates under vibration

Guilherme Ferreira Gomes et al.

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION (2018)

Article Engineering, Civil

Strain gauge placement optimization for structural performance assessment

K. Zhou et al.

ENGINEERING STRUCTURES (2017)

Article Acoustics

Influence of model errors in optimal sensor placement

Loris Vincenzi et al.

JOURNAL OF SOUND AND VIBRATION (2017)

Article Engineering, Mechanical

Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications

Edwin Reynders et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)

Article Computer Science, Interdisciplinary Applications

Comparison between coupled local minimizers method and differential evolution algorithm in dynamic damage detection problems

Loris Vincenzi et al.

ADVANCES IN ENGINEERING SOFTWARE (2013)

Article Engineering, Mechanical

Sensor placement for modal identification

Cyrille Stephan

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)

Article Engineering, Mechanical

Optimal sensors placement and spillover suppression

Tomas Hanis et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)

Article Engineering, Mechanical

The effect of prediction error correlation on optimal sensor placement in structural dynamics

Costas Papadimitriou et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2012)

Article Acoustics

Optimal sensor placement for spatial lattice structure based on genetic algorithms

Wei Liu et al.

JOURNAL OF SOUND AND VIBRATION (2008)

Article Acoustics

Optimal sensor placement methodology for parametric identification of structural systems

C Papadimitriou

JOURNAL OF SOUND AND VIBRATION (2004)

Article Engineering, Civil

Optimal sensor placement for fault detection

K Worden et al.

ENGINEERING STRUCTURES (2001)