Optimal Sensor Location Research Articles

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm

The detection of leaks in time series flow systems is crucial for efficient and integrated industrial processes. This is especially true when daily demand patterns differ, as this results in fluctuations in the snapshots of water consumption that are commonly used as the basis for placing sensors to detect leaks. This paper introduces a novel method in which the genetic algorithm (GA) is applied to find optimal sensor locations and to enhance the accuracy of leak detection in time series flow data. The method consists of two steps. Firstly, the GA is used to identify the optimal sensor locations using a specific fitness function that accounts for flow patterns, system topology, and leak characteristics. The novelty of the proposed method lies in the weighting scheme of the fitness function, which takes into consideration the frequency of events and the magnitude of leaks at potential locations. Secondly, the selected sensor locations are integrated with an advanced time series data analysis to locate leaks. In this technique, the most consistently performing locations are dynamically selected over time, allowing the model to adapt to varying conditions to maintain optimal sensor placement. Experiments were conducted on a simulated time series flow system with known leak scenarios to evaluate the performance of the proposed method. The results demonstrated the superiority of our GA-based sensor placement strategy in terms of leak detection accuracy and efficiency compared to other methods.•We developed a model called GA-Sense for sensor placement strategy by considering flow patterns to maximize leak detection and localization capabilities.•GA-Sense uses time series data to find strategic sensor locations to identify abnormal flow patterns indicative of leaks.•This approach enhances the accuracy and efficiency of leak detection and localization compared to alternative methods.

An investigation of optimal sensor locations for multi-type sensors considering modeling uncertainties by Bayesian system identification

This study investigates optimal sensor locations using multi-type sensors for strain recovery. Weighted least squares estimation is used for strain recovery, and the optimal sensor locations are evaluated by minimizing the sum of the covariance of modal displacement coordinates. Here, modeling uncertainty is taken into account by using Bayesian system identification. The present study evaluates multiple optimal sensor locations considering modeling uncertainties and quantifying their plausibility using multi-type sensors. First, a 2-D truss is studied by using an exhaustive search. It has been observed that when strain and acceleration data are fused, the sum of the variances of the modal displacement coordinate estimates is not sensitive to the locations of the acceleration sensors and that multiple optimal solutions exist. In order to obtain a unique solution and extract maximum information from acceleration data too, the sum of the variances of the estimates of a quantity defined as the pseudo-modal acceleration coordinates is also used as a performance measure. Further investigation using Bayesian system identification has revealed that the optimal sensor location with maximum plausibility is the same as the one obtained from the nominal model. Next, the methodology is studied using the 3-D truss model of a bridge in Jhansi, India, and a genetic algorithm is used for optimization. It has been observed that the variance of the estimates is a better measure for selecting the optimal sensor locations than the Fisher information because it can detect ill-conditioning. Finally, the methodology is applied to the experimental data obtained from a four-story shear building to gain further insights. Here also, it has been observed that the variance of the estimates rightly predicts the order of the optimal sensor locations, unlike the Fisher information. When the experimental data are used for Bayesian system identification, it has been noticed that all the predicted OSLs correspond to the one predicted by the nominal model.

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.

Selecting high-quality non-sensor locations from reconstructed dynamic responses of double-layer cylindrical reticulated shells considering step excitations

Because long-span spatial structures have a large number of nodes and members, monitoring tasks such as model updating and damage detection necessitate a large amount of high-quality test information. More test information can be obtained by reconstructing dynamic responses for non-sensor areas. However, because reconstruction errors are unknown, current study cannot tell whether a non-sensor location has high reconstruction accuracy or not. Thus, a novel technique for selecting high-quality non-sensor locations from reconstructed dynamic responses obtained solely from finite sensor test information was suggested. Firstly, dynamic responses are reconstructed by linearly combining only a few fixed contributing modes. This neatly converts the issue of reconstructing dynamic responses at non-sensor locations to that of estimating contributing mode coefficients (i.e., generalized coordinates). An iterative strategy for determining optimal sensor locations was proposed in order to achieve unbiased estimation of contributing mode coefficients at each moment. The dynamic responses at non-sensor locations were naturally reconstructed using estimates of contributing mode coefficients. Secondly, following the reconstruction error analysis, a correlation coefficient index was proposed to select high-quality non-sensor locations with high reconstruction accuracy in a reliable manner. As a typical representative of spatial structures, a double-layer cylindrical reticulated shell was taken as the example to validate the proposed method. Numerical results reveal that by using the proposed method, dynamic responses at non-sensor locations can conveniently be reconstructed and many high-quality non-sensor locations can reliably be selected. This explorative work is conducive to increasing the quantity of high-quality test information in a single dynamic test by using very few sensors, with test efficiency greatly improved and test cost significantly saved.

The Influence of External Parameters on the Ripeness of Pumpkins.

Growing pumpkins in controlled environments, such as greenhouses, has become increasingly important due to the potential to optimise yield and quality. However, achieving optimal environmental conditions for pumpkin cultivation requires precise monitoring and control, which can be facilitated by modern sensor technologies. The objective of this study was to determine the optimal placement of sensors to determine the influence of external parameters on the maturity of pumpkins. The greenhouse used in the study consisted of a plastic film for growing pumpkins. Five different sensors labeled from A1 to A5 measured the air temperature, humidity, soil temperature, soil humidity, and illumination at five different locations. We used two methods, error-based sensor placement and entropy-based sensor placement, to evaluate optimisation. We selected A3 sensor locations where the monitored data were close to the reference value, i.e., the average data of all measurement locations and parameters. Using this method, we selected sensor positions to monitor the influence of external parameters on the maturity of pumpkins. These methods enable the determination of optimal sensor locations to represent the entire facility environment and detect areas with significant environmental disparities. Our study provides an accurate measurement of the internal environment of a greenhouse and properly selects the base installation locations of sensors in the pumpkin greenhouse.

Optimization of number of wireless temperature sensors using clustering algorithm for deep learning algorithm-based Kimchi quality prediction

The production and storage of kimchi in a factory require monitoring temperature and humidity to maintain optimal quality. Recently, smart packaging devices such as wireless sensors have been used, but their installation and maintenance are costly and require additional resources. In this study, kimchi is packaged in polyethylene packages and placed in plastic baskets. Thirty baskets are stacked in the middle of the storage room. Wireless sensors placed inside the baskets collect temperature data alongside the kimchi for 60 days. The data is sent to a server through communication unit using 4G network. Two clustering algorithms, Competitive Learning Neural Network (CLNN) and k-means, are used to determine the optimal number and location of sensors. Deep learning algorithms, specifically Long Short-Term Memory (LSTM), predict the acidity and ripeness grade of the kimchi. The study found that the optimal number of temperature sensor groups for CLNN was 6 for acidity and 4 for ripeness grade classification. Increasing the number of sensors improved the kimchi acidity prediction, and the accuracy of ripeness classification increased with the number of sensors. The location of the wireless sensors was determined based on the heights of the stacked plastic baskets using the CLNN + LSTM model. This work presents a process for optimizing sensor placement and evaluating kimchi quality during storage and fermentation using clustering and deep learning techniques to maintain optimal quality.

Overview of Onera acoustic active control activities in helicopter cabin

Within a helicopter cabin, passengers are in close proximity to disturbing sources that contribute to interior noise: main and tail rotors, engines, main gearbox and aerodynamic turbulence. These sources generate bending vibrations of the entire tail boom, induced vibrations in the cabin at blade passing frequencies, transient vibrations of rotor blades and structure borne noise induced by gear meshing within gearboxes. Conventional passive systems, as trim panels or passive anti-resonance isolation systems, are still the main way to control the cabin noise. Nevertheless, (semi) active control techniques have been the subject of numerous studies for decades, especially to improve the performance of passive concepts in low frequencies. Unfortunately, many turned out to be unreliable or completely inapplicable because problems of robustness, time convergence of algorithms, but also a difficult identification of optimal locations for actuators and sensors or a high added mass and electrical power. The objectives of this paper is to describe in details the research projects in active noise control carried out at Onera since the 1990s, i.e. from numerical modeling and laboratory tests to flight tests in helicopter cabin. This overview will feed into the reflection on the renewal of active vibro-acoustic control within different laboratories.

Improving Water Distribution Network Models with Data Assimilation: Unleashing the Power of Real-Time Improvements

Ensuring a reliable water supply in the face of changing conditions and growing demand is a critical global challenge. Water distribution networks (WDNs) are essential infrastructure, but traditional modeling methods based on historical data often struggle to adapt in real-time and integrate new information. In order to lower model errors in WDN models, this study explores the use of a Data Assimilation (DA) method that makes use of the Ensemble Kalman Filter. The study explores the effectiveness of the DA method, and a novel Greedy Algorithm (GA) for optimizing the location of sensors. The study shows that the DA method improves the WDN model and the GA is able to successfully determine optimal sensor locations. It was observed that increasing the number of sensors in the WDN increased the effectiveness of the DA method. This study highlights the potential of data assimilation to improve WDN modeling. Water utilities can gain from more precise predictions, greater system performance, and efficient decision-making for the management and maintenance of water distribution networks by allowing models to adapt and improve dynamically

A New Optimal Sensor Location Method for Double-curvature Arch Dams: A Comparison with the Modal Assurance Criterion (MAC)

Determining the optimal location of sensors in order to identify modal parameters in large structures such as dams is one of the most important and widely used topics in damage detection and health monitoring of structures. In this research, the modal parameters including the natural frequency and mode shape of two arched concrete dams have been calculated using the finite element method for healthy and damaged dams. The reduction of the elastic modulus of concrete in different parts and percentages has been used as the degree of damage. Then, using the modal confidence criterion (MAC) method, the optimal location of the sensors is determined, then the results of this method are compared with the new method. The results show that in both dams, the new method matches the MAC method with 90% accuracy. This new method is a fast and suitable measure to determine the optimal location of sensors in arched concrete dams.

Bayesian Optimal Experimental Design for Race Tracking in Resin Transfer Moulding

A Bayesian inference formulation is applied to the Resin Transfer Moulding process to estimate bulk permeability and race-tracking effects using measured values of pressure at discrete sensor locations throughout a preform. The algorithm quantifies uncertainty in both the permeability and race-tracking effects, which decreases when more sensors are used or the preform geometry is less complex. We show that this approach becomes less reliable with a smaller resin exit vent. Numerical experiments show that the formulation can accurately predict race-tracking effects with few measurements. A Bayesian A-optimality formulation is used to develop a method for producing optimal sensor locations that reduce the uncertainty in the permeability and race-tracking estimates the most. This method is applied to two numerical examples which show that optimal designs reduce uncertainty by up to an order of magnitude compared to a random design.

Experimental Investigation of Mean Radiant Temperature Trends for a Ground Source Heat Pump-Integrated Radiant Wall and Ceiling Heating System

Mean radiant temperature (MRT) is one of the six primary factors that determine thermal comfort in a given thermal environment. In this study, the average radiant temperature was determined using a calculation method based on the surrounding surface temperatures and view factors. The present study specifically investigated the use of calculated radiant temperature, compared to measured radiant temperature, for predicting the mean vote (PMV) and percentage of dissatisfied (PPD) comfort parameters. The method was validated by the experimental measurements via the black sphere thermometer at five different reference points in a test room, including radiant panels on the ceiling and walls. By using global thermometer measurements, the proposed approach achieved a high degree of compatibility and an accuracy of 0.17 °C, which was the difference between calculated and measured values. The results demonstrated the reliability of the procedure using view factors and surrounding surface temperatures to calculate the radiant temperature in the designated test room; here, a straightforward method for evaluating the thermal conditions of an office room and determining the optimal location of an air temperature sensor in PMV-controlled radiant systems was also proposed. This study contributes to the increasing field of research on thermal comfort and offers knowledge that is beneficial for the design and optimization of indoor environments.

Multi-objective sensor placement optimization in SHM systems with Kriging-based mode shape interpolation

Sensor location optimization plays a key role in the application and development of structural integrity monitoring methodologies, especially in large mechanical structures. Given the existence of an effective damage detection and identification procedure, the question of how many and where to place the acquisition points (sensors) so that the monitoring system operates at peak efficiency arises. In this study, an innovative methodology is proposed in order to maximize the quality of modal information and minimize the number of sensors in the SHM system. To maximize the quality of modal information, it considered the reconstruction of mode shapes using Kriging interpolation. The study was carried out on plate-type composite material structures for initial validation and later applied and validated on a main rotor blade of the AS-350 helicopter. The initial modal information (modal deformation) was obtained through the finite element method, and the multi-objective Lichtenberg algorithm was used in the complex optimization process. The proposed method presented in this work allows for the best possible distribution of a minimum and sufficient number of acquisition points in a structure in order to obtain more modal information for a better modal reconstruction from kriging interpolation of these minimum points. Numerical examples and test results show that the proposed method is robust and effective for distributing a reduced number of sensors in a structure and at the same time guaranteeing the quality of the information obtained, even in noisy situations. Numerical results considering both the simple and complex geometric cases show that the proposed combined FS-kriging method is effective in distributing a finite number of sensors on the structures and at the same time guaranteeing the quality of the modal information obtained. The results also indicate that the layout of sensors obtained by multi-objective optimization does not become trivial and symmetrical when a set of modes is considered in the objective function formulation. The proposed strategy is an advantage in modal testing as it is only necessary to acquire signals at a limited number of points, saving time and operating costs in vibration-based processes.

SensCon: Embedding Physiological Sensing into Virtual Reality Controllers

Virtual reality experiences increasingly use physiological data for virtual environment adaptations to evaluate user experience and immersion. Previous research required complex medical-grade equipment to collect physiological data, limiting real-world applicability. To overcome this, we present SensCon for skin conductance and heart rate data acquisition. To identify the optimal sensor location in the controller, we conducted a first study investigating users' controller grasp behavior. In a second study, we evaluated the performance of SensCon against medical-grade devices in six scenarios regarding user experience and signal quality. Users subjectively preferred SensCon in terms of usability and user experience. Moreover, the signal quality evaluation showed satisfactory accuracy across static, dynamic, and cognitive scenarios. Therefore, SensCon reduces the complexity of capturing and adapting the environment via real-time physiological data. By open-sourcing SensCon, we enable researchers and practitioners to adapt their virtual reality environment effortlessly. Finally, we discuss possible use cases for virtual reality-embedded physiological sensing.

Pile defect assessment using distributed temperature sensing: fundamental questions examined

Thermal integrity testing has been successfully used to assess the quality of cast-in-place piles for the past decade. It employs temperature data measured during concrete curing to identify defects along the piles’ length. However, the uptake of this technology has been rather limited in the piling industry. The main concerns are that the method is not standardised and its reliability is not well understood. In order to address these, there are a number of fundamental questions that need to be explored in more detail, including (a) the optimum time to conduct the assessment, (b) the defect thermal impact, (c) the zone of influence on temperature sensors, (d) the minimum detectable size of a defect and (e) the associated optimum sensor location required. In this paper, experimental and numerical studies were conducted to examine these questions. Fibre optic sensors were employed on model concrete piles in laboratory tests to provide fully distributed temperature data throughout the curing process. The test results showed that the optimum time to assess the defects is approximately at 60% of the time to reach peak temperature and the minimal detectable defect size, using the currently available optical fibre sensor technology, is 4% of the cross-sectional area. In addition, the thermal influence of different defect sizes is presented. Following this, it is shown in the paper that the minimum numbers of sensor cables required to identify defects with cross-sectional areas of 4%, 5% and 8% are eight, six and four cables, respectively. The optimum layout of these sensor cables within a pile cross-section has also been discussed. When specifying pile instrumentation for integrity assessment, the findings of this paper enable practising engineers to make informed judgements in relation to the size of defects they would like to detect (and hence the associated risk this entails) together with the corresponding instrumentation layout required.

Research on fast optimal reference sensor placement in active road noise control

The noise reduction performance of the active road noise control (ARNC) system highly depends on the location of the reference sensors. Generally, the optimal sensor locations are evaluated by calculating the multiple coherence function (MCF) between all possible reference signal combinations with road noise. However, this trial-and- error method becomes time-consuming when the number of candidate locations is large. The transfer path analysis method can select the optimal sensor locations quickly while with low accuracy. Therefore, this article proposes two fast optimal sensor placement (FOSP) methods, namely, Wiener filter (WF)-FOSPand the MCF-FOSP, respectively. In both methods, the sensors are iteratively extended to the desired number, and each added sensor maximizes the predicted noise reduction of this iteration loop. Numerous ARNC simulations based on measured signals are conducted to illustrate the performance of the proposed two methods in terms of efficiency and accuracy. The results demonstrate that the WF-FOSP method provides the best comprehensive performance. The data analysis for one operating condition takes three minutes, and the absolute error is within 5% with respect to the benchmark. In addition, two schemes are discussed to obtain a set of sensor locations compatible with the noise reduction requirement of different operating conditions. The sensor locations can achieve a maximum average noise reduction of 7.29 dB(A) under four operating conditions.

Optimized Sensor Placement of Water Supply Network Based on Multi-Objective White Whale Optimization Algorithm

The optimization of sensor locations in water distribution networks has been extensively studied. Previous studies of highly sensitive nodes are usually distributed in a certain area, which leads to redundant information in the sensor network. This is because these studies do not consider that the impact is different when a leak occurs in different nodes. In this study, sensitivity functions of different nodes were obtained according to the influence of the leakage of each node on the water distribution network. Combined with the water pressure correlation and water pressure sensitivity between nodes, the monitoring range of monitoring points and the water demand of covering nodes of monitoring points were taken as objective functions to build an optimal layout model. Taking a pipeline network in Qingdao as an example, the model was solved by using multi-objective White Whale Optimization and NSGA-II. By comparing the operation results of the four cases, it was found that the monitoring points found using multi-objective White Whale Optimization show better searching ability in terms of the sensitivity functions of different nodes.

Optimal sensor placement for structural parameter identification of bridges with modeling uncertainties

The uncertainties in the numerical models used for the optimal sensor placement (OSP) studies of civil infrastructures, specifically bridges, considerably affect the results. These effects can be more prominent if the modeling uncertainties are of a kind that significantly alters the mode shapes of the structure, such as the boundary conditions of the model. Yet, these effects on the results of OSP analysis remain unexplored, and there are no available methodologies to address all types of model uncertainties in OSP for civil infrastructures. This research presents a new framework to determine the optimal sensor locations to identify the modal properties of bridges under severe modeling uncertainties and its application on a railway bridge. The framework includes finite element model generation and a sensitivity study to select the most influential parameters that change the dynamic response of the bridge. The selected parameters are used in Monte Carlo simulations, and the results enable quantifying the relative amount of information presented at the candidate sensor locations. This information is combined with the spatial position of the candidate sensor locations, and a hierarchical clustering algorithm is used to obtain the optimal sensor locations and the number of sensors. In addition, the OSP analysis is carried out using the Effective Independence method to contribute to the state-of-the-art literature that investigates the uncertainties in OSP for civil infrastructures and uses this method.

Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach

Accurate measurement of micro-climates that include temperature and relative humidity is the bedrock of the control and management of plant life in protected cultivation systems. Hence, the use of a large number of sensors distributed within the greenhouse or mobile sensors that can be moved from one location to another has been proposed, which are both capital and labor-intensive. On the contrary, accurate measurement of micro-climates can be achieved through the identification of the optimal number of sensors and their optimal locations, whose measurements are representative of the micro-climate in the entire greenhouse. However, given the number of sensors, their optimal locations are proven to vary from time to time as the outdoor weather conditions change. Therefore, regularly shifting the sensors to their optimal locations with the change in outdoor conditions is cost-intensive and may not be appropriate. In this paper, a framework based on the dense neural network (DNN) is proposed to predict the measurements (temperature and humidity) corresponding to the optimal sensor locations, which vary relative to the outdoor weather, using the measurements from sensors whose locations are fixed. The employed framework demonstrates a very high correlation between the true and predicted values with an average coefficient value of 0.91 and 0.85 for both temperature and humidity, respectively. In other words, through a combination of the optimal number of fixed sensors and DNN architecture that performs multi-channel regression, we estimate the micro-climate of the greenhouse.

Analysis and Minimization of Race Tracking in the Resin-Transfer-Molding Process by Monte Carlo Simulation.

A numerical analysis of the influence of race tracking on dry spots formation and the accuracy of permeability measurement during the resin-transfer-molding process is presented. In the numerical simulation of the mold-filling process, defects are randomly generated, and their effect is assessed by a Monte Carlo simulation method. The effect of race tracking on the unsaturated permeability measurement and dry spots formation is investigated on flat plates. It is observed that the race-tracking defects located near the injection gate increase up to 40% of the value of the measured unsaturated permeability. The race-tracking defects located near the air vents are more likely to generate dry spots, whereas those near the injection gates have a less significant influence on dry spots generation. Depending on vent location, it has for instance been shown that the dry spot area can increase by a factor of 30. Dry spots may be mitigated by placing an air vent at a suitable location based on the numerical analysis results. Moreover, those results may be helpful to determine optimal sensor locations for the on-line control of mold-filling processes. Finally, the approach is successfully applied to a complex geometry.

A genetic programming-based optimal sensor placement for greenhouse monitoring and control.

Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson's correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility.