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sensor placement optimization for composite aircraft structures: A multi-objective Kriging-based approach

COMPOSITE STRUCTURES 2025年 353卷

作者： Mello, Felipe Martarella de Souza Gomes, Guilherme Ferreira Fed Univ Itajuba UNIFEI Mech Engn Inst Itajuba Brazil

Efficient sensor placement is critical for optimizing structural health monitoring (SHM) systems, particularly for composite aircraft structures. The challenge lies in determining the optimal number and positions of sensors to balance monitoring accuracy with resource efficiency. This study proposes a novel multi-objective optimization methodology that integrates Kriging interpolation for mode shape reconstruction with the Lichtenberg algorithm for sensor placement optimization. The approach minimizes the number of sensors while maximizing signal quality and minimizing interpolation error. The methodology is validated on both composite plates and the main rotor blade of an AS-350 helicopter using finite element modal analysis data. The results demonstrate significant reductions in the number of sensors required while maintaining high accuracy in capturing the structure's dynamic response, showing the effectiveness of the proposed method for SHM applications in complex composite structures.

关键词： sensor placement optimization Composite structures Kriging interpolation Lichtenberg Algorithm Modal analysis

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A unified framework for enhancing inverse finite element method through strain pre-extrapolation and sensor placement optimization

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 234卷

作者： Li, Kelu Xiao, Longfei Wei, Handi Kou, Yufeng Shan, Meng Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China SJTU Sanya Yazhou Bay Inst Deepsea Sci & Technol Hainan 572024 Peoples R China

The inverse finite element method (iFEM) is a powerful tool for shape sensing, but its effectiveness is often constrained by economic and spatial constraints that prevent sensor coverage. This paper presents a unified framework to enhance iFEM, comprising two key components. The first is a novel strain pre-extrapolation method, GPR-MCKF, which incorporates both spatial and temporal correlations from measurement data. To reduce the computational demand of Gaussian process regression (GPR) when handling large datasets, maximum correntropy Kalman filtering (MCKF) is used to handle temporal correlation, based on the space-time separability of the kernel and the state-space equation of Gaussian process. The second component is a sensor placement optimization (SPO) method with general applicability, introducing an innovative objective function based on the Fisher information matrix (FIM) of the extrapolated strain. This objective function links the two components. Moreover, with constant noise levels and a fixed kernel function, this objective function depends solely on the measurement locations, meaning the results of SPO are determined exclusively by the structural geometry. This objective function, combined with the number of sensors, forms a bi-objective optimization problem, which is solved using the multi-objective Lichtenberg algorithm (MOLA). Finally, numerical and experimental examples validate the framework's effectiveness, robustness, and general applicability in shape sensing with limited sensors, demonstrating its potential for practical applications to complex and large-scale structures.

关键词： iFEM sensor placement optimization Strain pre-extrapolation Gaussian process regression Maximum correntropy Kalman filter Fisher information

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sensor placement optimization applied to laminated composite plates under vibration

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2018年第5期58卷 2099-2118页

作者： Gomes, Guilherme Ferreira da Cunha Jr, Sebastiao Simoes Lopes Alexandrino, Patricia da Silva de Sousa, Bruno Silva Ancelotti Jr, Antonio Carlos Univ Fed Itajuba Mech Engn Inst Itajuba Brazil

The location optimization of sensors is a essential problem in structural health monitoring systems. Taking the cost of sensors into account, it is uneconomical to install sensors on every part of a structure and moreover in aeronautical industry, the weight is a crucial factor. In this paper, a optimal placement optimization of sensor locations for structural health monitoring systems is studied. Several techniques of optimization of sensors are approached and applied in a shell structure. The structure, a laminate of carbon fiber, was modeled by the finite element method (FEM) and then subject to free vibration. Genetic algorithms (GAs) are then employed to locate the best sensor distribution to cover a specific number of low frequency modes. Numerical results have demonstrated the overall efficiency of sensor delivery methods. Specific problems occurred, especially regarding the method of effective independence, being less efficient and discrepant in relation to the other methods employed. In summary, the results obtained in this paper provide an optimal position for sensors in real SHM systems and experiments.

关键词： sensor placement optimization Structural health monitoring Genetic algorithm Modal data Composite plates

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sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm

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EVOLUTIONARY INTELLIGENCE 2020年第4期13卷 571-591页

作者： Gomes, Guilherme Ferreira Pereira, Joao Vitor Purcino Fed Univ Itajuba UNIFEI Mech Engn Inst Itajuba Brazil

The structural monitoring of mechanical systems is an extremely important task for ensuring its performance and structural health. To overcome limitations of traditional non-destructive inspections (NDIs), damage identification techniques have been developed from global indicators, mainly those based on modal data. In this study, damages are identified by solving an inverse problem. A fuselage model of an E190 aircraft is considered and the firefly algorithm (FA) metaheuristic is applied to solve the inverse problem in order to identify structural damages (location and severity). The method is then solved in two main fronts: (1) the direct problem using finite element analysis and (2) the inverse problem by minimizing an objective function. Evaluating modal response at many points on a large-scale structure can become prohibitive. For this, a method of optimizing sensors is performed using the Fisher information matrix (FIM). Results are compared considering the sensor placement optimization problem. It is noticed that optimized sensors contribute to an improvement in the identification of damages, mainly for complex and large-scale structures. The proposed optimized damage identification process using FIM-FA has the potential to be extended to a wide range of SHM applications in complex structures. Hence, traditional NDIs have many shortcomings due to the complexity of large-scale structures as well as modern design structures and may not be practicable if the structure has restricted access. Accordingly, an enhanced damage identification method is developed in order to better handle measurement data to find structural changes (or damages) in complex aerospace structures.

关键词： Damage identification Inverse problem Firefly algorithm Fuselage sensor placement optimization

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sensor placement optimization with Fault Diagnosable Constraint and Its Application in Spacecraft Attitude Control System 34

Sensor Placement Optimization with Fault Diagnosable Constra...

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第三十四届中国控制会议

作者： GAO Sheng GUO Yanning MA Guangfu XU kang Department of Control Science and Engineering Harbin Institute of Technology

ISBN: (纸本)9781467374439

The sensor placement optimization problem with fault diagnosable constraint is investigated in this ***,in order to diagnose actuators' faults in the spacecraft attitude control system,a fault- measuring point correlation matrix model is established based on the directed graph ***,through constructing optimal index in terms of placement cost of measuring point under the fault diagnosable constraint,a sensor placement optimization algorithm is proposed by using modified discrete binary particle swarm ***,to validate the effectiveness of the proposed algorithm,a typical sensor placement optimization problem for momentum wheel fault detection and isolation is carried out,results show that the proposed algorithm can quickly obtain the global optimal solution.

关键词： Fault diagnosis Directed graph sensor placement optimization Modified discrete binary particle swarm algorithm Momentum wheel

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sensor placement optimization FOR DAMAGE EVALUATION OF BRIDGES SUBJECT TO SHIP COLLISION

SENSOR PLACEMENT OPTIMIZATION FOR DAMAGE EVALUATION OF BRIDG...

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1st International Postgraduate Conference on Infrastructure and Environment

作者： Guo, Y. L. Ni, Y. Q. Chen, S. K. Hong Kong Polytech Univ Dept Civil & Struct Engn Kowloon Hong Kong Peoples R China

ISBN: (纸本)9789881731128

Hundreds of ship-bridge collision accidents were reported around the world every year. Post-collision damage and condition assessment is of significant importance for decision making on if closure of the bridge to traffic is necessary and for planning the consequent bridge strengthening or retrofitting. An on-line structural health monitoring (SHM) systems makes it feasible to monitor the dynamic responses during ship collision and detect post-collision damage of the instrumented bridge using monitoring data. The damage information contained in the monitoring data, which is critical for damage detection, however is largely dependent on the sensor layout. In this paper, a sensor placement optimization (SPO) method targeting post-collision damage detection of bridges is proposed. The objective of the proposed method is to select the optimal sensor set so that the measured data are most informative for damage detection. The information entropy is used as the optimization criterion. Considering all possible ship-bridge collision scenarios, the optimal sensor placement is obtained using a multi-objective optimization algorithm which simultaneously minimizes the information entropy index for each scenario. One advantage of the proposed method is that the optimal sensor placement is effective under all possible ship collision scenarios. Also, the method can identify the regions with high sensitivity with respect to the changes in damage monitoring model (DMM) parameters. Thirdly, the method can guarantee a redundancy of sensors in the most sensitive regions;in particular, it leaves a certain freedom to determine the critical elements in DMMs. The proposed method is applicable in practice to determining the sensor placement prior to field testing with intent to identify post-collision damage. The cable-stayed Ting Kau Bridge (TKB) in Hong Kong is employed to demonstrate the feasibility and effectiveness of the proposed method.

关键词： Ship-bridge collision damage detection sensor placement optimization sensitivity information entropy cable-stayed bridge

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sensor placement optimization using Random Sample Consensus for Best Views Estimation

Sensor Placement Optimization using Random Sample Consensus ...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Costa, Carlos M. Veiga, Germano Sousa, Armando Thomas, Ulrike Rocha, Luis INESC TEC Ctr Robot Ind & Intelligent Syst Porto Portugal Univ Porto Fac Engn Porto Portugal Tech Univ Chemnitz Robot & Human Machine Interact Lab Chemnitz Germany

ISBN: (纸本)9798350301212

The estimation of a 3D sensor constellation for maximizing the observable surface area percentage of a given set of target objects is a challenging and combinatorial explosive problem that has a wide range of applications for perception tasks that may require gathering sensor information from multiple views due to environment occlusions. To tackle this problem, the Gazebo simulator was configured for accurately modeling 8 types of depth cameras with different hardware characteristics, such as image resolution, field of view, range of measurements and acquisition rate. Later on, several populations of depth sensors were deployed within 4 different testing environments targeting object recognition and bin picking applications with increasing level of occlusions and geometry complexity. The sensor populations were either uniformly or randomly inserted on a set of regions of interest in which useful sensor data could be retrieved and in which the real sensors could be installed or moved by a robotic arm. The proposed approach of using fusion of 3D point clouds from multiple sensors using color segmentation and voxel grid merging for fast surface area coverage computation, coupled with a random sample consensus algorithm for best views estimation, managed to quickly estimate useful sensor constellations for maximizing the observable surface area of a set of target objects, making it suitable to be used for deciding the type and spatial disposition of sensors and also guide movable 3D cameras for avoiding environment occlusions.

关键词： Best views estimation sensor placement optimization random sample consensus bin picking

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Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

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ADVANCES IN STRUCTURAL ENGINEERING 2021年第10期24卷 2255-2266页

作者： Saleem, Muhammad Mazhar Jo, Hongki Univ Arizona Dept Civil & Architectural Engn & Mech 1209 E 2nd St Tucson AZ 85719 USA Univ Engn & Technol Lahore Dept Civil Engn Lahore Pakistan

Although a lot of different types of sensors are available in the market only a limited number of sensors can be installed on a structure. Proper placement of these sensors plays a vital role in effectively achieving the objectives of a monitoring system. sensor placement becomes especially critical in the case of bridges where the applied loading keeps on changing its location. A sensor layout that provides good quality structural response estimates for a given applied loading may not yield acceptable results for a different loading arrangement. Further, usually different types of sensors are installed on a structure e.g. strain gauges and accelerometers. These sensors measure different physical quantities having different units and orders of magnitude thus cannot be easily incorporated in a sensor placement optimization (SPO) process. So, this research work proposes a multi-objective sensor placement optimization approach that can effectively deal with different types of sensor measurements and spatially varying loading such as in the case of bridges. The proposed method employs an augmented Kalman filter (AKF) for structural response estimation and a multi-objective genetic algorithm for SPO. The AKF can effectively estimate structural response using a few heterogeneous noisy measurements while incorporating the modeling error. The effectiveness of the proposed method is demonstrated using a numerical example of a 3D truss bridge structure. The results show that the proposed multi-objective optimization method yields a sensor arrangement that remains effective against spatially varying dynamic loading.

关键词： multi-objective genetic algorithm sensor placement optimization structural response estimation

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SPARSE sensor placement optimization FOR CLASSIFICATION

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SIAM JOURNAL ON APPLIED MATHEMATICS 2016年第5期76卷 2099-2122页

作者： Brunton, B. W. Brunton, S. L. Proctor, J. L. Kutz, J. N. Univ Washington Dept Biol Seattle WA 98195 USA Univ Washington Inst Neuroengn Seattle WA 98195 USA Univ Washington Dept Mech Engn Seattle WA 98195 USA Univ Washington Dept Appl Math Seattle WA 98195 USA Inst Dis Modeling Bellevue WA 98005 USA Univ Washington Dept Appl Math Seattle WA 98195 USA

Choosing a limited set of sensor locations to characterize or classify a high-dimensional system is an important challenge in engineering design. Traditionally, optimizing the sensor locations involves a brute-force, combinatorial search, which is NP-hard and is computationally intractable for even moderately large problems. Using recent advances in sparsity-promoting techniques, we present a novel algorithm to solve this sparse sensor placement optimization for classification (SSPOC) that exploits low-dimensional structure exhibited by many high-dimensional systems. Our approach is inspired by compressed sensing, a framework that reconstructs data from few measurements. If only classification is required, reconstruction can be circumvented and the measurements needed are orders-of-magnitude fewer still. Our algorithm solves an l(1) minimization to find the fewest nonzero entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space;these entries represent sensor locations that best inform the decision task. We demonstrate the SSPOC algorithm on five classification tasks, using datasets from a diverse set of examples, including physical dynamical systems, image recognition, and microarray cancer identification. Once training identifies sensor locations, data taken at these locations forms a low-dimensional measurement space, and we perform computationally efficient classification with accuracy approaching that of classification using full-state data. The algorithm also works when trained on heavily subsampled data, eliminating the need for unrealistic full-state training data.

关键词： sensor placement optimization classification compressed sensing l(1) minimization enhanced sparsity feature selection machine learning

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Model-based supervised sensor placement optimization to detect propellant leak in a liquid rocket engine

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ACTA ASTRONAUTICA 2022年 195卷 234-242页

作者： Omata, Noriyasu Satoh, Daiwa Tsutsumi, Seiji Kawatsu, Kaname Abe, Masaharu Japan Aerosp Explorat Agcy Sagamihara Kanagawa 2525210 Japan Japan Aerosp Explorat Agcy Tsukuba Ibaraki 3058505 Japan Ryoyu Syst Co Ltd Nagoya Aichi 4450024 Japan Hitachi Ltd Hitachi Ibaraki 3191292 Japan

The sensor selection for leak detection and diagnosis in a reusable liquid rocket engine was studied. Sufficient amounts of normal and leaked training data were generated by Monte Carlo simulations considering variations in system conditions based on past firing test results. A multivariate supervised analysis, in which measurements are linearly projected onto a vector that characterizes the difference between distributions of normal and leaked cases, successfully detected the simulated leaks that could not be detected by conventional univariate red-line judgment and unsupervised principal component analysis. Significant sensors for leak detection were selected using the greedy approach based on the detection performance score. It was found that only 11 of the 27 sensors were sufficient to maintain the detection performance.

关键词： sensor placement optimization Supervised fault detection Model-based fault detection Liquid rocket engine

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