Optimal Sensor Location Research Articles

Optimization of sensor location and method analyzing for in-situ monitoring of concrete containment structures: a case involving embedded moisture sensors

Optimization of sensor location and method analyzing for in-situ monitoring of concrete containment structures: a case involving embedded moisture sensors

A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States

Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to capture the full extent of current pollution variability. Prior research on best sensor placement for monitoring fine particulate matter (PM2.5) pollution is scarce: most studies do not span areas larger than a medium-sized city or examine timescales longer than 1 week. Here we present a pilot study using multiresolution dynamic mode decomposition (mrDMD) to identify the optimal placement of PM2.5 sensors from 2000 to 2016 over the contiguous United States. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 d to over a decade to capture air pollution variability. We find that the mrDMD algorithm identifies more high-priority sensor locations in the western United States than those expected along the eastern coast, where a large number of Environmental Protection Agency (EPA) PM2.5 monitors currently reside. Specifically, 53% of mrDMD optimized sensor locations are west of the 100th meridian, compared to only 32% in the current EPA network. The mrDMD sensor locations can capture PM2.5 from wildfires and high pollution events, with particularly high skill in the west. These results suggest significant gaps in the current EPA monitoring network in the San Joaquin Valley in California, northern California, and in the Pacific Northwest (Idaho, and Eastern Washington and Oregon). Our framework diagnoses where to place air quality sensors so that they can best monitor smoke from wildfires. Our framework may also be applied to urban areas for equitable placement of PM2.5 monitors.

Corrosion damage identification for reinforced concrete beam using embedded piezoelectric transducer: Numerical simulation

This paper performed a numerical simulation on corrosion damage identification using embedded piezoceramic (PZT) transducers. Three-dimensional finite element (FE) model of a full-scaled RC beam was generated with experimental validation, where three groups of PZT sensors in depth, width and longitudinal directions of the beam were simulated to evaluate corrosion damage rate correlated to concrete/rebar parameters including mass loss, cross-section loss, elastic modulus, strength and bond-slip relationship at steel–concrete interface. Simulation and experimental results indicated that corrosion severity was well reflected in resonance characteristics of PZT admittance and its derived root mean square deviation (RMSD) as well as mechanical impedance. Based on the results, a new sensitivity index was proposed for evaluating corrosion evolution, which suggested that the optimal sensor location was in the depth direction of the beam within a scope of 400 mm, and corrosion evolution as corrosion initiation and crack propagation stages could be effectively identified.

Optimal Sensor Placement for Azimuthal Acoustic Modes in Cylindrical Ducts

To identify azimuthal acoustic modes in cylindrical ducts, multiple sensors must be mounted at different azimuthal locations. For large-scale, expensive facilities or testing campaigns, such as engine testing, access points can be limited and must be chosen with great care amongst a host of other competing instrumentation and operational needs. This paper introduces a methodology to determine the optimal sensor locations, defined as the locations that minimize the variance of the estimated amplitude of azimuthal eigenmodes, for a given number of sensors and candidate acoustic modes . Given that access may not always be possible at these optimal locations, the same formulation also quantifies the tradeoffs with nonoptimal sensor locations. We present explicit results for optimal sensor locations for identifying a single mode, as well as illustrative computed results for optimal locations to identify multiple modes. For identifying a single mode, it is shown that the optimal placements decrease the uncertainty of the estimated wave amplitudes by and that of the estimated pressure by . We also present a table summarizing optimal locations for and various combinations of the first three azimuthal modes.

Correlation Coefficient Based Optimal Vibration Sensor Placement and Number.

Vibration sensors are mostly used for fault diagnoses of machines or structures. If more sensors are applied, more accurate fault diagnosis is possible. However, it will obviously cost more. There are many approaches to optimize the number and installation location/point of vibration sensors for more efficient fault diagnosis. Existing methods require a great deal of computational throughput for optimization when considering many mode frequencies with points where vibration sensors are likely to be installed. This paper proposes a practical way of optimizing the sensor installation point considering many mode frequencies with lots of places for sensor installation. FEA was conducted to identify displacement values of each frequency in the candidate points. Then, correlation coefficients were applied to the FEA result to optimize the installation location and number of vibration sensors. Taking into account cases where the number of sensors has been set by users, FIM was applied. The correlation coefficient optimized the candidate points where 24,252 vibration sensors were to be installed and reduced this to 10 points. FIM, which was not suitable for directly optimizing sensor locations because it required a lot of computational throughput and was usually applied to evaluate other methods, is now applicable to candidate points that have been reduced by the correlation coefficient. This paper does not draw the best optimal sensor location but presents a way to apply to large-scale or complicated forms with a little computational throughput.

Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Establishing a robust thermal error model is crucial for thermal error compensation of machine tools. In this paper, an improved exponential model for thermal error modeling was proposed. First, the improved exponential model is established by combining the exponential model and the temperature-dependent model. Second, the improved fruit fly optimization algorithm is used to determine the parameters. Finally, two strategies are used to select the optimal sensor location from six potential ambient temperature sensors. The ambient temperature data is proved to be effective in improving the robustness of the model. The average root mean square error of the proposed model, the exponential model without ambient temperature, and the linear model are 1.18 μm, 1.79 μm, and 2.70 μm, respectively. The modeling results show that the proposed model has high accuracy and strong robustness, which is suitable for workshop environment and variable conditions in thermal error modeling of machine tools.

In-Wing Pressure Measurements for Airspeed and Airflow Angle Estimation and High Angle-of-Attack Flight

Small uncrewed aerial vehicles (UAVs) capable of stable, controlled stalled descent offer the possibility for a wider operating envelope and for precise, steep approaches and landings. However, the aerodynamics of stalled flight are inherently complex and underexplored compared to flight regimes with attached flow. This paper presents a number of advancements in these areas—particularly focusing on pressure sensors for estimating flow states. This paper presents a modular, open-source design for in-wing, flow-based differential pressure sensor arrays for estimating flow states without the requirement of fragile pitot tubes or airflow vanes. An extensive, publicly available set of wind tunnel and flight test results from a small UAV with 24 in-wing pressure sensors is provided. The data contain a wide range of incidence angles, including deep stalled states. This paper also presents data-driven regression models for predicting airflow angles and airspeed using only pressure information. Our results demonstrate predictions for airspeed, angle of attack (AoA), and angle of slip (AoS) with root mean square error (RMSE) of , 0.20°, and 2.83°, respectively, using wind tunnel data and all 24 sensors. Finally, this paper provides a placement analysis to determine the optimal sensor locations for estimating airflow data with fewer pressure sensors. Models trained and validated with real flight data show airspeed, AoA, and AoS predictions with RMSE of , 1.54°, and 4.07°, respectively, using data from only three pressure sensors each.

Optimal sensor placement techniques for modal identification of historical masonry structures

Since destructive tests are not allowed for historical structures, numerical model updating using accelerometers has gained a lot of attraction in the last decade. Furthermore, another application of structural health monitoring is damage detection for near-real-time monitoring of cultural heritage assets of infrastructures such as masonry bridges. However, high cost is the main problem that discourages the use of large-scale structural health monitoring systems, and a modal pretest analysis is required to plan and optimize the modal tests procedure. For this purpose, various optimal sensor placement (OSP) techniques have been developed to derive the operational modal analysis results with a minimum number of sensors, leading to a lower cost. In this study, various OSP techniques have been applied to optimize sensor placement in two selected case studies. The first case study is a two-span masonry arch bridge in Rhodes, Greece and the second is a stone masonry tower located in Tønsberg, Norway. Baseline finite element models were developed before performing the ambient vibration tests and model updating process. The optimum sensor locations were detected using various techniques, and a comparative study was conducted on the results. Furthermore, the effect of considering soil-structure interaction on the OSP results was investigated.

流れ場統合飛行制御の耐乱気流性能のシミュレーションによる評価

In the future UAM (Urban Air Mobility) society, a wide variety of small aircraft, including drones, are cooperatively operated in urban spaces at lower altitudes. Unlike in undisturbed high-altitudes for large airliners, the UAM airspace might be atmospherically unstable, and severe turbulence like sudden crosswinds and downdrafts between buildings should be assumed. Such microclimate phenomena inherent to urban space are difficult to predict, and are a major threat to the safe operation of small lightweight aircrafts flying at low speeds. Hence, there is a great need for powerful attitude stabilization system that can back up manual control and typical SAS (Stability Augmentation System). In this study, the Flow Field Integrated Flight Control is proposed as a solution to this problem. The surface pressure field is used to obtain wind gust characteristics and improve flight stability. The feedback gain is tuned based on real-time modeling of the relationship between the aircraft motion and the surface pressure field, including the effect of turbulence. The proposed design method does not require any models in advance, such as aerodynamic model, mass properties, and turbulence models. The optimal pressure sensor location is also investigated by simulations.

Integrity Testing of a Platform‐Pile System Using a Sensor Array and Wavenumber Domain Analysis

Appropriate impact and sensor locations must be chosen in pile integrity tests to prevent three‐dimensional effects caused by the torsional and flexural modes. The three‐dimensional characteristics cause high‐frequency interference, especially in bridge and wharf piles. A method is required to minimize the high‐frequency interference without reducing the accuracy of the pile integrity test. A multivelocity integrity test method is proposed based on a sensor array and frequency‐wavenumber (FK) domain analysis to eliminate high‐frequency interference and reduce the errors in the output of integrity tests of platform‐pile systems. FK filtering is performed to eliminate the spatial alias frequency and separate the upward and downward wavefield and the vibration modes in an integrity test of a platform‐pile system. The optimum sensor location to minimize the influence of interference signals is at the bending plane relative to the impact location. Using a sensor array reduces the influence of the sensor location on the test results and minimizes the requirements for determining the location of the excitation point and sensors in the traditional low‐strain integrity testing (LST) method, thereby improving the applicability of this method.

Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects

The hourly traffic flows between various origin–destination (OD) pairs fluctuate by time of day and day of the year. These multi-period OD demands are statistically correlated with one another because of the inter-relationships of travel patterns over time. In this paper, with a focus on the covariance relationship of OD demands in multiple periods, a novel model is proposed for optimizing the allocations of multi-type traffic sensors by minimizing the uncertainty of OD demand estimates. In the proposed model, both the number and locations of multi-type traffic sensors, including point sensors and automatic vehicle identification (AVI) sensors, are optimized simultaneously with consideration of budget and associated constraints. The mathematical properties of the proposed model are studied to show the significance of multi-period OD flow covariance in the sensor location problem and to examine the trade-off between point sensors and AVI sensors. The firefly algorithm is adapted to solve the problem of multi-type traffic sensor locations for multi-period OD demand estimation. To enhance the estimation efficiency, a Kalman filter method based on the principal component analysis is adopted to extract the essential features of the OD demands and then estimate multi-period OD demand. Numerical examples are presented to demonstrate the effects of OD demand covariance in multiple periods for the multi-type sensor allocation problem.

Optimal sensor placement to detect ruptures in pipeline systems subject to uncertainty using an Adam-mutated genetic algorithm

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

A Shoe-Integrated Sensor System for Long- Term Center of Pressure Evaluation

In clinical, the center of pressure (CoP) is commonly used for accessing the stability of a person’s postural control, which is highly associated with various neurological diseases and movement disorders such as Alzheimer’s disease, Parkinson’s disease, chronic ankle instability. Such a disease usually has a long development or rehabilitation process which requires long-term CoP monitoring. The current CoP evaluation process does not meet the requirement, as it is often complicated and expensive through either the lab-based equipment or the clinical evaluation procedure. Different wearable sensor-based systems with less cost and restrictions have emerged, but their way of CoP calculation requires deliberate calibration of the positions of their sensors, which are not feasible in daily CoP monitoring. In this study, we developed a long-term CoP monitoring system in a smart-shoe form. First, a thin and flexible smart insole with optimal sensor locations was designed to be compact and energy sufficient for a whole-day usage. Then, a user-friendly app on the smartphone with a cloud-based data managing system was developed for applications in both clinical and home environments. Additionally, a simplified CoP estimation model was created without the need for calibration. Lastly, a machine learning-based human activity recognition method was incorporated to make the CoP detection process more automatic. Through a thorough validation test with the clinical level lab equipment, our system can generate the CoP measurements with high accuracy.

Sensor Deployment Strategy and Traffic Demand Estimation with Multisource Data

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

An algorithm for finding optimal sensor placement and model uncertainty in data assimilation applied to Riemann problems

The sensor location and selection of model noise parameters play an utmost important role in the performance of data assimilation methods. Suboptimal sensor placement may lead to incomplete information and incorrectly chosen noise parameters can cause filter divergence. Till now, these issues were addressed separately, but in this work, we consider both. We present an algorithm that finds the optimal sensor location and non-Gaussian model noise parameters not only separately but also simultaneously. The optimal location was determined for a fixed number of sensors and the root mean square error was used to assess the accuracy of the data assimilation scheme. A direct search method was implemented and, due to the stochastic nature of the filtering method, we used a sample average approximation approach. To prove the usefulness of the algorithm, we selected Riemann problems in one and two dimensions, where the solutions contain shock waves, rarefaction waves, and contact discontinuities. A high order finite volume weighted essentially non-oscillatory scheme was used for the numerical approximation. We used the Rusanov Riemann solver for the numerical flux and the three-stage third-order strong stability preserving Runge-Kutta scheme for the time advancing. Numerical experiments showed that optimizing both sensor location and model noise parameters gives more accurate results without suffering from filter divergence. Uniform distribution of the sensors and manually setting the noise parameters caused the filter to fail to track the truth, especially near the discontinuities.

Optimization of network sensor location for full link flow observability considering sensor measurement error

Full link flow observability problem is to identify the minimum set of links to be installed with traffic sensors in a vehicular traffic network, so that unobserved link flows can be fully inferred from the observed link flows based on the correlation between links. Inevitably, the observed link flows are subject to measurement errors, which will accumulate and propagate in the inference of unobserved link flows. This study aims to identify the minimum observed link set for the installation of counting sensors to reach full link flow observability, while minimizing the effect of sensor measurement errors on the inference of unobserved link flows. We propose a network sensor location model considering the propagation of measurement errors in the link flow inference process. Mathematically, we formulate the problem as min–max and min-sum integer linear programs. The objective function attempts directly to minimize the accumulated inference errors caused by the propagation of sensor measurement errors. When the sensors have the uniform measurement error, it is equivalent to minimizing the maximum or cumulative number of observed links required for the link flow inference of each or all unobserved links. The genetic algorithm (GA) is adopted to solve the proposed optimization model, and the results commend the optimal sensor location scheme with the minimum inference error for full link flow observability. The approach is useful for deploying long-term planning and link-based applications in traffic networks.

Optimal temperature sensor placement in buildings with buoyancy-driven natural ventilation using computational fluid dynamics and uncertainty quantification

Natural ventilation and cooling can significantly reduce building energy consumption, but inherent variability in the boundary and operating conditions makes robust design and operation a challenging task. Computational models can be leveraged to evaluate the effects of these uncertainties and support robust design; however, the model accuracy should be thoroughly assessed, ideally through validation with carefully designed full-scale measurements. This study presents a novel approach for using computational fluid dynamics (CFD) simulations with uncertainty quantification to optimize temperature sensor placement in buildings with buoyancy-driven natural ventilation, such that the measurements are representative of the volume-averaged temperature, while also supporting analysis of the spatial variability in the temperature field. The approach uses a polynomial chaos expansion method to quantify the effect of the variability in the boundary and initial conditions on the predicted buoyancy-driven flow and indoor temperature field. Results for an operational building that employs night-time cooling are analyzed to identify sensor locations that will support robust observation of the time series of the volume-averaged temperature and the temperature range. The approach is shown to significantly reduce the potential error between the temperature recorded at a random location and the true volume-average temperature, while also providing a representative measure of the temperature range in the building. The results indicate that this approach can also support identifying optimal locations for temperature sensors used for operational control.

Novel Optimal Multisensor Placement for Indoor Rectilinear Line-of-Sight Coverage

Optimal multisensor placement/deployment for an arbitrary indoor geometry still remains very challenging nowadays. It is preferable to minimize the number of sensors which can still cover the entire indoor space with a balanced load. However, this multi-objective problem is quite complex in practice. In this work, we will study this critical multisensor placement problem mathematically and algorithmically and propose a systematic approach to tackle this problem. In this paper, we focus on the line-of-sight (LoS) coverage within a rectilinear indoor environment and the sensors’ locations are restricted on the perimeter. Our proposed new approach consists of two stages. First, a partitioning algorithm is designed to partition an arbitrary rectilinear geometry into a number (as few as possible) of feasible rectilinear subareas, each of which can be fully covered by a sensor located somewhere on its external perimeter. Second, the average squared Euclidean distance from a sensor to an arbitrary point within its coverage area is taken as an additional objective measure. By minimizing such average squared Euclidean distance, one can find the optimal sensor location. In our new approach, the first stage is related to solving the art gallery problem (AGP) based on discrete optimization while the second stage is based on continuous optimization. Therefore, our proposed new approach involves both continuous and discrete optimization schemes. By simulations, we also show that the number of sensors placed by our new algorithm is equal to or less than the theoretical upper-bound established in the existing AGP study for numerous rectilinear geometries. Moreover, we also show that by minimizing the average squared Euclidean distance, we maximize the average received signal strength indoors using the prevalent Feko wireless-channel emulator. Our proposed new systematic approach can also deal with practical scenarios involving internal obstacle(s) and sensing-range restriction.

Optimal Sensor Location in a Smart-Shirt to Measure Accurate Tidal Volumes During Abdominal and Thoracic Respiration

Abstract The determination of respiratory parameters via respiration induced surface movements of the upper body has been the subject of research for many years. The displacements of 102 motion capture markers were evaluated in this study in terms of their information content with respect to the tidal volume recorded in parallel using a spirometer. Independent of the breathing types (spontaneous breathing, abdominal breathing, or chest breathing), the number and the location of sensors in a smart shirt to obtain tidal volume information was determined. Only 9 of 102 sensors were sufficient to obtain breathing volume information.

Optimal placement of tsunami sensors with depth constraint.

Tsunamis are destructive natural disasters that can cause severe damage to property and the loss of many lives. To mitigate the damage and casualties, tsunami warning systems are implemented in coastal areas, especially in locations with high seismic activity. This study presents a method to identify the placement of near-shore detection sensors by minimizing the tsunami detection time, obtained by solving the two-dimensional shallow water equations (SWE). Several benchmark tests were done to establish the robustness of the SWE model, which is solved using a staggered finite volume method. The optimization problem is solved using particle swarm optimization (PSO). The proposed method is applied to different test problems. As an application, the method is used to find the optimal location of a detection sensor using data from the 2018 Palu tsunami. Our findings show that detection time can be significantly reduced through the strategic placement of tsunami sensors.