Optimal Sensor Layout Research Articles

On optimizing the sensor spacing for pressure measurements on wind turbine airfoils

Abstract. This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force. The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39 %–55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups. While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils.

Optimal Sensor Placement with Minimal Predicted Posterior Variance

The performance of each monitoring system critically depends on the sensor layout and typical optimization criteria are based on the accessibility to the structure on site, experience with similar structures, and an optimal signal-to-noise ratio. More advanced criteria aim to optimize the feature extraction process, and this paper aims to develop a sensor placement strategy for optimal damage diagnosis. The approach is based on Bayesian updating and the optimization goal is to find a sensor layout that is the most sensitive to small changes in the examined system parameters. It is shown that such layouts manifest themselves through a minimum variance in the posterior distribution. Using Kalman filters, the posterior variance can be “predicted” based on the measurement error and numerical models, so no data from and no simulations in the damaged state are required. By employing a polynomial chaos expansion (PCE)-based Kalman filter, the method becomes applicable for structures with many measurement channels, multiple examined system parameters, and for a large number of sensor combinations. Gaussian distributions are assumed for both system inputs and outputs. For proof of concept, the sensor placement is optimized on a numerical cantilever beam, demonstrating that the method can efficiently find the optimal sensor layout. Moreover, arbitrary measurement quantities (inclinations, vibrations, etc.) or damage-sensitive features (statistical values, modal parameters, etc.) can be used as observations, and multiple system parameters can be considered at the same time. Among other factors, an optimal sensor layout leads to large measurement responses, a small measurement error, and more advanced statistical quantities that will be elaborated on in detail.

An optimal sensor layout method based on noise reduction estimation for active road noise control

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications.

Predicting shear stress distribution on structural surfaces under internal solitary wave loading: A deep learning perspective

The density of the ocean varies unevenly along the vertical axis. In the presence of external disturbances, internal solitary waves (ISWs) are generated. The strong shear flow field induced by ISW seriously threatens the operational safety of marine structures. Therefore, it has become a hot spot to study the force law of marine structures in ISW. The existing studies are conducted when the ISW parameters are known. However, ISW is not visible in real situations, which leads to difficulties in obtaining ISW parameters. Therefore, it is of great engineering value to accomplish real-time force prediction of marine structures without knowing the ISW parameters in advance. To fill the gap, this study proposes a novel hydrodynamic prediction model with a sensor array as the sensing system and a deep learning algorithm as the decision-making system. The model successfully achieves accurate prediction of the shear stress on the cylinder in the ISW. In addition, a technique for optimizing sensor placement is proposed. This will help identify critical regions in the graphical representations to enhance exploration of flow field information. The results demonstrate that the prediction accuracy of the optimized sensor layout scheme surpasses that of randomly deployed sensors. As a result, this study will provide an important assurance for the safe operation of marine structures.

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

An information-entropy-inspired optimal layout method for structural stress sensors of supersized open caissons

The layout of sensors directly influences the effectiveness of structural stress monitoring of open caissons during the sinking process. In this study, an optimal layout method for structural stress sensors is proposed inspired by information entropy. The information gains of different sinking states from different layout locations of structural stress sensors are calculated, and the important layout locations are identified through the calculation of information gains. According to these important layout locations, the optimal sensor layout scheme is obtained. The proposed method was validated via an engineering case study of the Changtai Yangtze River Bridge Project. The results show that the proposed method successfully determined the optimal sensor layout scheme and that the number of required structural stress sensors decreases by 51.5% compared with the number of structural stress sensors deployed in the engineering case. With all R2 values greater than 0.96, the optimal scheme has high prediction accuracy for three sinking states: sinking speed, transverse height difference and longitudinal height difference. In addition, the spatial distribution of the important layout locations was identified. This study is meaningful for obtaining high-quality monitoring data of structural stress to both ensure the steady sinking of open caissons and reduce monitoring costs.

Optimization of sensor deployment sequences for hazardous gas leakage monitoring and source term estimation

Nowadays, chemical safety has attracted considerable attention, and chemical gas leakage monitoring and source term estimation (STE) have become hot spots. However, few studies have focused on sensor layouts in scenarios with multiple potential leakage sources and wind conditions, and studies on the risk information (RI) detection and prioritization order of sensors have not been performed. In this work, the monitoring area of a chemical factory is divided into multiple rectangles with a uniform mesh. The RI value of each grid node is calculated on the basis of the occurrence probability and normalized concentrations of each leakage scenario. A high RI value indicates that a sensor at a grid node has a high chance of detecting gas concentrations in different leakage scenarios. This situation is beneficial for leakage monitoring and STE. The methods of similarity redundancy detection and the maximization of sensor RI detection are applied to determine the sequence of sensor locations. This study reveals that the RI detection of the optimal sensor layout with eight sensors exceeds that of the typical layout with 12 sensors. In addition, STE with the optimized placement sequence of the sensor layout is numerically simulated. The statistical results of each scenario with various numbers of sensors reveal that STE is affected by sensor number and scenarios (leakage locations and winds). In most scenarios, appropriate STE results can be retained under the optimal sensor layout even with four sensors. Eight or more sensors are advised to improve the performance of STE in all scenarios. Moreover, the reliability of the STE results in each scenario can be known in advance with a specific number of sensors. Such information thus provides a reference for emergency rescue.

Multi-sensor optimal placement of rotor-bearing system based on fault diagnosability

In view of the current situation that there are few studies on whether the fault signals that may occur in the rotor bearing system can be identified and analyzed in the sensor arrangement, fault diagnosability is applied to the optimal arrangement of the sensors in the rotor bearing system. The preliminary layout of the multi-sensor is carried out by analyzing the transmission path of the vibration signals in the rotor bearing system and based on the minimum path. Secondly, the nonparametric kernel density estimation method calculates the state information’s probability density function. The K-L divergence value is further used to measure fault detectability, fault separability, and fault diagnosability. Then, the correlation function method is used to fuse the multi-sensor information, and the optimal sensor layout is given with the fault diagnosability as an index. Finally, the validity of the method is proved by the experimental laboratory data and the correlation coefficient.

Sensor layout strategy for source term estimation of external pollution sources in urban neighbourhoods

The estimation of source parameters is essential when hazardous substances are leaked or released around urban neighbourhoods, and the layout of the sensor plays an important role in source term estimation (STE). Currently, few sensor layout strategies exist for external pollution sources in urban neighbourhoods. In this study, the STE performance of 10 strategies for arranging sensors around urban neighbourhoods was analyzed, and the impact of each factor in the layout strategy on the STE performance was discussed. The results show that the number of sensors has a considerable influence on the STE performance, and better STE performance can be achieved by adding a large number of sensors. However, the STE performance could no longer be noticeably improved when the number of sensors increased to a certain extent. With the same number of sensors, a better STE performance can be obtained by arranging sensors around buildings rather than at the crossing of city streets. An optimal sensor layout strategy is proposed for the STE of external pollution sources, which uses fewer sensors while considering a good STE performance. Finally, the full posterior distributions of the source parameters with different sensor numbers were compared to understand the impact of the sensor amount on the estimation of the source location and strength.

An Optimal Sensor Layout Using the Frequency Response Function Data within a Wide Range of Frequencies.

This study presents iterative optimal sensor placement (OSP) techniques using the modal assurance criterion (MAC) and the effective independence (EI) algorithm. The algorithms use the proper orthogonal mode (POM) extracted from the frequency response functions (FRFs) of dynamic systems within a wide range of frequencies. The FRF-based OSP method proposed in this study has the merit of reflecting dynamic characteristics, unlike the mode shape-based method. Evaluating the MAC values and the EI indices at each iteration, the DOFs of low contribution to the objective function of candidate sensor DOFs are deleted from master DOFs and moved to slave DOFs. This process is repeated until the sensor number corresponds with the master DOFs. The validity of the proposed methods is illustrated in an example, the sensor layouts by the proposed methods are compared, and the layout inconsistency between the MAC and the EI techniques is analyzed.

Sensor placement with optimal damage detectability for statistical damage detection

Damage diagnosis based on global structural vibrations critically depends on the sensor layout, in particular when a small number of sensors is used for large structures under unknown excitation. This paper proposes a sensor placement strategy that yields an optimized sensor layout with maximum damage detectability in selected structural components. The optimization criterion is based on the Fisher information, which quantifies the information that the damage-sensitive feature carries on the design parameters of structural components, such as material constants or cross-sectional values. It is evaluated using a finite element model, and considers the statistical uncertainties of the damage-sensitive feature. The methodology is shown for the stochastic subspace-based damage detection method, but can be applied to any damage-sensitive feature whose distribution can be approximated as Gaussian. It is suitable to find the optimal layout for a fixed number of sensors and to choose an appropriate number of sensors. Since the Fisher information is defined component-wise, the sensor layout can be tuned to become more sensitive to damage in local structural components, such as damage hotspots, non-inspectable components, or components that are critical for the safety and serviceability of the structure. For proof of concept, the sensor layout on a laboratory beam is optimized based on numerical simulations, and it is showcased that the optimal sensor layout leads to the highest damage detectability for experimental data.

Optimal sensor placements using modified Fisher information matrix and effective information algorithm

This article presents an optimal sensor placement algorithm for modifying the Fisher information matrix and effective information. The modified Fisher information matrix and effective information are expressed using a dynamic equation constrained by the condensed relationship of the incomplete mode shape matrix. The mode shape matrix row corresponding to the master degree of freedom of the lowest-contribution Fisher information matrix and effective information indices is moved to the slave degree of freedom during each iteration to obtain an updated shape matrix, which is then used in subsequent calculations. The iteration is repeated until the target sensors attain the targeted number of modes. The numerical simulations are then applied to compare the optimal sensor placement results obtained using the number of installed sensors, and the contribution matrices using the Fisher information matrix and effective information approaches are compared based on the proposed parameter matrix. The mode-shape-based optimal sensor placement approach selects the optimal sensor layout at the positions to uniformly allocate the entire degree of freedom. The numerical results reveal that the proposed F-based and effective information–based approaches lead to slightly different results, depending on the number of parameter matrix modes; however, the resulting final optimal sensor placement is included in a group of common candidate sensor locations. However, the resulting final optimal sensor placement is included in a group of common candidate sensor locations.

Optimal sensor placement through expansion of static strain measurements to static displacements

Optimal sensor placement is used to establish the optimal sensor quantity and layout. In this study, the minimum quantity and locations of measurement sensors were assumed to satisfy the constraint conditions of the optimal sensor placement. A set of strain data in a truss structure was expanded to another set of displacements corresponding to the entire degrees of freedom from the relationship between the strain and displacement. It indicates to reduce the number of sensors because the strain depends on the displacements in a finite element model. The damaged truss element was traced using the expanded data that satisfied the prescribed constraints. The proposed optimal sensor placement method has a merit to explicitly determine the optimal sensor locations without any numerical scheme and statistical methods. The method was applied to the damage detection of a single-damaged truss structure. It was shown that the optimal sensor placement method depended on the sensor layout irrespective of the same quantity of sensors. In addition, a numerical example was used to compare sensitivities to damage detection based on the sensor placement and the existence of external noise contained in the measurement data.

An Optimization Method for the Layout of Soil Humidity Sensors Based on Compressed Sensing

In the farmland Internet of Things, to achieve precise control of production, it is necessary to obtain more data support, which requires the deployment of many sensors, and this will inevitably bring about high investment and high‐cost problems. This paper mainly studies the optimization of sensor placement in the agricultural field. Through compressed sensing and algorithm optimization, the number of sensors used is reduced and the cost is reduced on the premise of ensuring the effect. At present, there are many mature sensor layout optimization methods, but these methods will have incomplete parameters due to experimental conditions and environmental factors. They are more suitable for structural health monitoring and lack research in agricultural applications. Considering that the sensor layout optimization can be converted into the characteristics of image compression selection and the compression effect of the compressed sensing theory is better, therefore, this paper proposes a sensor layout optimization method based on compressed sensing. Due to the structural characteristics of the existing measurement matrix in the compressed sensing theory, the specific position distribution of the optimized sensor layout cannot be obtained directly. This paper improves the existing sparse random measurement matrix to determine the number of sensors required for a given region and the function of the specific location of each sensor. The experimental results show that soil moisture can be measured with a small error of 0.91 by using 1/3 of the original sensor number. The result of data reconstruction using 1/6 of the original sensor is average, and the average error is up to 1.68, which is suitable for the environment with small data fluctuation.

A two-stage multi-damage detection approach for composite structures using MKECR-Tikhonov regularization iterative method and model updating procedure

Modal kinetic energy (MKE)-based feature has been widely employed for the selection of optimal sensor layout; however, its development to the field of structural damage identification has still received less attention. The article hence is performed to fill in this research gap by proposing a two-stage multi-damage detection approach for composite structures by using MKE change ratio (MKECR) and model updating procedure, taking into consideration both spatially-incomplete measurements and noise. In the first stage, the MKECR-based method is used to construct the damage equation set and then a Tikhonov regularization-based iterative technique is employed to solve this equation set for the purpose of damage localization. In the second stage, the model updating procedure using Lightning Attachment Procedure Optimization (LAPO) algorithm is established and implemented for damage severity estimation, in which the MKE feature is introduced here as an objective function for the first time. Finally, the performance of the proposed two-stage damage detection approach is investigated through two numerical examples of different composite structures (beam and plate). The obtained results from the simulation examples reveal that the proposed identification approach can identify multi-damage locations and estimate their damage severities with satisfactory accuracy, even under spatially-incomplete measurements and high measurement noise.

Optimal layout of sensors in large-span cable-stayed bridges subjected to moving vehicular loads

To obtain the health status of long-span cable-stayed bridges, multiple sensors are applied to the health monitoring system for data collection. The optimal layout of sensors that aims to obtain as much structural information as possible with fewer sensors is important to ensure the effectiveness of the health monitoring system. Sensors are usually placed in typical locations where the structural response is obvious, and most studies utilize static response for the determination of sensor location. In fact, bridges primarily suffer the dynamic load, of which the response has a significant impact on the structural health. In this article, an optimal sensor layout method for a long-span cable-stayed bridge based on dynamic response is proposed under the consideration of vehicle–bridge coupled vibration. With vehicle load applied onto different lanes, the dynamic responses of different bridge members are obtained, and the number and the location of cable force sensors are determined according to the distribution of cable dynamic coefficient DC, and the number and the location of displacement and strain sensors are determined according to the distribution of DGD and DGM, which are the dynamic load allowance for girder deflection and bending moment, respectively. The results prove that this method can reduce the number of sensors effectively and obtain bridge state information more perfectly.

Sensor positioning in the context of wave‐based damage identification in dams

AbstractDams and their adjourning reservoirs play an important role in the drinking water supply as well as in the flood defense. Their world‐wide use and possible massive devastation due to dam failures make the monitoring of the structural health of these huge structures imperative. Therefore, it is necessary to identify faults/damages in the dams' structure non‐invasively in order to proceed with the current use. Additionally, monitoring of these huge structures is very complex, time‐consuming and costly. To circumvent the aforementioned challenges, an inverse analysis, here an enhanced, regularized, cyclic full‐waveform inversion is proposed based on wavelengths between seismic and ultrasonic waves. Parameters of the dam material, such as stiffness, density and permeability, are estimated in synthetic experiments. Having this, an optimal design of experiment approach is used to identify the most informative sensor positions regarding the reconstruction quality. An optimized sensor layout may further improve the efficiency of the solution of the inverse problem.

Modal Kinetic Energy Change Ratio-based Damage Assessment of Laminated Composite Beams using Noisy and Incomplete Measurements

Modal kinetic energy (MKE) feature has been mostly employed for optimal sensor layout strategies; nevertheless, little attention is paid to use the feature to the field of structural damage detection. The article presents the extensive applicability of MKE change ratio (MKECR), a good damage sensitive parameter, to damage localization and quantification of laminated composite beams. The formulation of the parameter is based on the closed-form of element MKE sensitivity. The performance of the offered damage detection method is numerically verified by a clamped-clamped composite beam and a two-span continuous composite beam with different hypothetical damage scenarios. The influence of incomplete mode shapes, various noise levels as well as damage magnitudes on damage prediction results are also investigated. The obtained results from these numerical examples indicate that the offered method reliably localize the actually damaged elements and approximately estimate their severities, even under incomplete measurements at a high noise level.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Timing optimization for 3D silicon sensors

Looking forward to future High Luminosity LHC experiments, efforts to develop new tracking detectors are increasing. A common approach to improve track reconstruction efficiency in high pile-up conditions is to add time measurement per pixel with resolution smaller than 50 ps. Different sensor technologies are under development in order to achieve those performances, like low gain avalanche diodes and 3D sensors. 3D sensors are characterized by very fast charge collection times, but present some critical issues in timing due to their electrode configurations. The presence of zero electric field volumes inside the electrodes themselves and low electric field regions between same sign electrodes causes that the 3D sensor technology presents potentially a large time walk contribution which negatively affects time resolution. In order to reduce this error drastically, a detailed study based mostly on simulation has been done with main focus on the exploration for a timing optimized 3D sensor electrode configuration. To have a more detailed view of the timing performances, sensor operation was also simulated, using TCAD and other simulation tools developed specific for this application, and the results analysed. In this presentation a detailed overview of the modelling and simulation activity as well as their results, including also future steps will be presented. The output of this studies defines the optimal sensor layout for timing applications.

A new IMMU-based data glove for hand motion capture with optimized sensor layout

The number of people with hand disabilities caused by stroke is increasing every year. Developing a low-cost and easy-to-use data glove to capture the human hand motion can be used to assess the patient’s hand ability in home environment. While a majority of existing hand motion capture methods are too complex to be used for patients in residential settings. This paper proposes a new sensor layout strategy using the inertial and magnetic measurement units and designs a multi-sensor Kalman data fusion algorithm. The sensor layout strategy is optimized according to the inverse kinematics and the developed hand model, and the number of sensors can be significantly reduced from 12 in conventional systems to 6 in our system with the hand motion being completely and accurately reconstructed. Hand motion capture experiments were conducted on a healthy subject using the developed data glove. The hand motion can be restored completely and the hand gesture can be recognized with an accuracy of 85%. The results of a continuous hand movement indicate an average error under 15% compared with the common glove with full sensors. This new set with optimized sensor layout is promising for lower-cost and residential medical applications.