Optimal Sensor Research Articles

A non-invasive short range acoustic technique for liquid level measurement in containers

Techniques to measure liquid level in containers and vessels non-invasively are of great interest for the manufacturing, chemical industries, and energy sectors. Most commercial solutions use invasive techniques, which are not always practical and can lead to unsafe conditions when measuring devices encounter high pressures or hazardous chemical contents. In this study, a portable, non-invasive acoustic liquid level identification technique is developed. The technique relies on comprehensive analysis of acoustic guided wave modes in the container walls and their coupling to liquid contents to determine the optimal sensor separation distance and excitation signal characteristics, resulting in liquid level determination with high accuracy and precision. First, dispersion characteristics of guided wave propagation in container walls in contact with a liquid boundary are studied in detail. Based on the dispersion analysis a general-purpose short-range liquid level detection methodology is developed. The developed methodology is then validated using numerical wave propagation simulations and experiments on various metal containers with different physical characteristics, illustrating the versatility of the method. Compared to existing techniques, which are optimized for specific applications, the proposed method, due to its short range and non-invasive nature, has the potential for universal applicability on virtually any container, irrespective of its shape, size, wall-thickness, and whether it has any internal components.

A novel triple-structure coding to use evolutionary algorithms for optimal sensor placement integrated with modal identification

A novel triple-structure coding to use evolutionary algorithms for optimal sensor placement integrated with modal identification

Optimal arrangement of AE sensors for prestressed hollow slabs based on AE propagation characteristics

Acoustic emission (AE) health monitoring is crucial for effective data collection, rational monitoring of results, and economical engineering applications in prestressed hollow slabs. To achieve an optimal sensor arrangement, experiments were performed to measure AE attenuation with different propagation paths in both the longitudinal and transverse directions of a full-scale prestressed hollow slab. Then, the AE amplitude and AE energy are chosen as evaluation indexes to investigate the hybrid arrangement of AE sensors under multiple damage conditions. The results indicate that when the AE signal propagates along the longitudinal direction of the prestressed hollow slabs with the same propagation distance, the attenuation in the steel strand is smaller. Additionally, the attenuation in the concrete directly under the bottom strand is smaller than that in the concrete between the bottom strands. The attenuation of AE propagation is smaller in the complete hinge than in the defective hinge. In terms of transverse propagation, the attenuation of AE signals is less in the bottom slab concrete than in the bottom slab steel strands. At the longitudinal 1/3 section of the prestressed hollow slab, the amplitude attenuation rate shows a turning point, and the variation in the attenuation rate of the AE signal is relatively small. For greater efficiency, it is recommended to position the sensors in the middle part of the hollow slab hinge joint and the bottom slab cross section, as well as on the concrete surface directly under the bottom slab strand.

A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine

There are hundreds of various sensors used for online Prognosis and Health Management (PHM) of LREs. Inspired by the fact that a limited number of key sensors are selected for inflight control purposes in LRE, it is practical to optimal placement of redundant sensors for improving the diagnosability and economics of PHM systems. To strike a balance between sensor cost, real-time performance and diagnosability of the fault diagnosis algorithm in LRE, this paper proposes a novel Optimal Sensor Placement (OSP) method. Firstly, a Kernel Extreme Learning Machine-based (KELM) two-stage diagnosis algorithm is developed based on a system-level failure simulation model of LRE. Secondly, hierarchical diagnosability metrics are constructed to formulate the OSP problem in this paper. Thirdly, a Hierarchy Ranking Evolutionary Algorithm-based (HREA) two-stage OSP method is developed, achieving further optimization of Pareto solutions by the improved hypervolume indicator. Finally, the proposed method is validated using failure simulation datasets and hot-fire test-run experiment datasets. Additionally, four classical binary multi-objective optimization algorithms are introduced for comparison. The testing results demonstrate that the HREA-based OSP method outperforms other classical methods in effectively balancing the sensor cost, real-time performance and diagnosability of the diagnosis algorithm. The proposed method in this paper implements system-level OSP for LRE fault diagnosis and exhibits the potential for application in the development of reusable LREs.

Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a Circulating Fuel Reactor

Sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for advanced nuclear reactors. In Circulating Fuel Reactors (CFR), liquid fuel and coolant are homogeneously mixed, and thus these reactors will not have internal structures, making sensor positioning in the primary circuit, including the core, an unresolved problem, making most of the core blind to sensors. Thus, the possibility of estimating the system state in the whole domain using a few local measurements has important implications for safety, monitoring, and control both in nominal and accidental conditions. In this context, the integrated Model Order Reduction and Data Assimilation framework offers intriguing opportunities to reliably combine experimental data and background knowledge from a reduced mathematical model. This work discusses and applies innovative methods within this framework, based on the Generalized Empirical Interpolation and the Indirect Reconstruction algorithms, to a proposed concept of CFR. This work aims to identify the optimal sensor positioning within the core and assess the feasibility of reconstructing the quantities of interest starting only from transient sparse data on fuel temperature, possibly noisy, and testing the predictive capabilities of the discussed methods.

Strategic Placement of Accelerometers for Structural Health Monitoring of a Complex Unreinforced Stone Masonry Hindu Mandir

ABSTRACT The BAPS Hindu Mandir, recently constructed in Abu Dhabi, UAE, is a complex unreinforced stone masonry structure built from thousands of sculpted sandstone and marble pieces employing ancient Indian techniques called Shilp Shastra. The entire structure is substantially large with a footprint size of 5,100 m 2 and unique so that it is not covered by modern seismic design standards. Its performance was verified by conducting dynamic field tests presented herein. The most vulnerable substructure was identified based on both engineering judgement and modal analysis of the entire structure employing a detailed 3D finite element model, which was validated via the field experiments. A “local” model was developed for the identified vulnerable substructure which significantly reduced model complexity and allowed to overcome computational limitations. Based on the response of the local model, the relative importance of the sensor locations was determined via a Displacement Index method in addition to a reduction of the total variance of spectral accelerations using conditional probability theory. Through this approach, a methodology for selecting the optimal sensor placement with application on the complex unreinforced stone masonry Hindu Mandir is proposed.

Enhanced NO2 gas response of ZnO–Ti3C2Tx MXene nanocomposites by microwave irradiation

ZnO–Ti3C2Tx MXene nanocomposites with varying MXene contents (0.5, 1, 2, and 5 wt%) were synthesized for NO2 detection, and the impact of microwave (MW) irradiation time (1–8 min) on NO2 response was explored. It was observed that the sensor containing 2 wt% Ti3C2Tx MXene, irradiated for 5 min, indicated the best response of 42.65 to NO2 (10 ppm) at 300°C. Additionally, the optimal gas sensor demonstrated long-term stability (over six months), and reproducibility. The boosted NO2 response was ascribed to the creation of ZnO–MXene Schottky barriers, an increase in oxygen vacancies due to MW irradiation, a large surface area of the nanocomposite sensor, and the presence of surface groups on MXene. We confirmed the promising effects of MW irradiation in enhancing gas sensing, showcasing it as a cost-effective and readily available technique.

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function.

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.

Development of disposal SPCE pH sensor based on polypyrrole and cloth as conductive polymer

In fish farming, monitoring water quality is crucial for the health and growth of the fish. This field area needs a robust device to monitor the pH meter. In this research, a disposal pH sensor was developed from alternative materials that can offer a wider application area than the conventional glass sensor. Polypyrrole (PPy) membrane was applied to the carbon electrode and acted as a pH-sensitive material when combined with the counter electrode. The carbon electrode was made by printing the carbon paste into cotton-rayon cloth and electrodeposition processing to create a PPy layer on the electrode surface. Afterward, the working electrode was evaluated using cyclic voltammetry and ion-selective electrode potentiometry to optimize the performance. The experimental measurements show that the electrodeposition process at 4.5 A and 30 s creates PPy deposits with optimum sensor performance. pH response and stability tests have also been performed, as the results show that the pH sensor has met the requirement for Nernst effect and IUPAC standard. Finally, the sensor was validated using a reference pH meter to test the water sample acquired from local lake, river, and fishpond, where the result shows that the pH sensor gives accurate measurements.

Nano Application of Oil Concentration Detection Using Double-Tooth Ring Plasma Sensing

Based on the unique properties of optical Fano resonance and plasmonic-waveguide coupling systems, this paper explores a novel refractive index concentration sensor structure. The sensor structure is composed of a metal–insulator–metal (MIM) waveguide and two identically shaped and sized double-tooth ring couplers (DTR). The performance structure of the nanoscale refractive index sensor with DTR cavity was comprehensively assessed using the finite element method (FEM). Due to the impact of various geometric parameters on the sensing characteristics, including the rotation angles, the widths between the double-tooth rings, and the gaps between the cavity and the waveguide, we identified an optimal novel refractive index sensor structure that boasts the best performance indices. Finally, the DTR cavity sensor achieved a sensitivity of 4137 nm/RIU and Figure of merit (FOM) of 59.1. Given the high complexity and sensitivity of the overall structure, this nanoscale refractive index sensor can be applied to the detection of oil concentration in industrial oil–water mixtures, yielding highly precise results.

Construction of bayberry-like Cu2O/CuO and detection of trace Cl2 at low temperature

The effective detection of toxic and harmful gases is of great significance for protecting the environment and human health. The construction of heterostructures plays a crucial role in improving gas sensing performance of sensitive materials. In this study, an one-step solvothermal method was used to directly synthesize ∼2 μm bayberry-like Cu2O/CuO. Due to the formation of heterostructures, the gas sensing performance of the material is effectively improved. The optimal sensor based on Cu2O/CuO exhibits good gas sensing performance (S=45) to 10 ppm Cl2 at 50 ℃, with a detection limit as low as 300 ppb (S=1.33), good selectivity and reproducibility. Its excellent gas sensing performance can be attributed to the presence of oxygen vacancies in the material and the synergistic effect between Cu2O/CuO heterostructure components. The presence of oxygen vacancies increases the adsorption of oxygen ions on the material surface. The formation of heterostructures not only regulates the electronic structure of CuO, but also provides more adsorption sites for the target gas. Therefore, the bayberry-like Cu2O/CuO material can be used as a candidate sensitive material for low-temperature Cl2 gas detection.

A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems.

In this paper, a novel Multi-Objective Hypergraph Particle Swarm Optimization (MOHGPSO) algorithm for structural health monitoring (SHM) systems is considered. This algorithm autonomously identifies the most relevant sensor placements in a combined fitness function without artificial intervention. The approach utilizes six established Optimal Sensor Placement (OSP) methods to generate a Pareto front, which is systematically analyzed and archived through Grey Relational Analysis (GRA) and Fuzzy Decision Making (FDM). This comprehensive analysis demonstrates the proposed approach's superior performance in determining sensor placements, showcasing its adaptability to structural changes, enhancement of durability, and effective management of the life cycle of structures. Overall, this paper makes a significant contribution to engineering by leveraging advancements in sensor and information technologies to ensure essential infrastructure safety through SHM systems.

Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology

This paper proposes a novel deep-learning-enabled framework for abnormal data detection and recovery considering spatial-temporal correlation among sensors. The wavelet scattering transform is adopted to replace the first layer of one-dimension convolutional neural network classification model. The multiple typical sensor failure modes of sensor are identified including bias, drifting, gain, precision degradation, and three complete faults (constant; constant + noise; noise). The bidirectional long-short time memory (BLSTM) network is developed to recover abnormal data through positive and negative propagation. The BLSTM cell involving forget, input, and output gate is utilized to adaptively transmit valuable information of long-term data. Different sensor configurations are conducted to select the optimal input sensors of BLSTM model. The proposed models are then validated by three engineering cases. The results show that the failure mode diagnosis and recovery accuracy are over 90 %. Sensor selection has a large impact on the data reconstruction, and selecting sensors with higher correlation can achieve a higher accuracy. In addition, the data recovery accuracy of the proposed BLSTM model surpasses that of traditional long-short time memory models.

AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data‐driven control

AbstractThe mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross‐disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The CTF further supports sustainable and open‐source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.

Underground Mine Safety and Health: A Hybrid MEREC-CoCoSo System for the Selection of Best Sensor.

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of "Ease of Installation" as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model's performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

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.

Textile wearable system for knee angle monitoring in three planes

Introduction
 Monitoring biomechanics is crucial in sports and rehabilitation, and frontal knee angle is of special interest in these applications. Current solutions – optical motion capture (OMC), or inertial measurement units suits – are costly, spatially constrained, and impractical for use in daily life. Textile-based wearable systems are a valuable alternative for unobtrusive movement monitoring. Textile-based wearables for knee angle monitoring have mostly been used for sagittal angle prediction, however, frontal knee angle measurement is more challenging. We investigated the design and performance of a smart garment for the detection of knee joint angles in three planes during different activities.
 Methods
 We equipped a pair of tight pants with four helical auxetic yarn capacitive strain sensors (Cuthbert et al., 2022) placed close to the knees. The exact positioning was optimized with an OMC study: markers were placed in potential sensor locations (Gholami et al., 2019) and the pairs of markers whose distance had the highest mutual information with knee angles were selected for sensor placement. A healthy participant performed walking and turning around, and knee ab/adduction activities wearing the sensorized prototype. The latter activity emphasized knee motion in the frontal and transverse planes. The capacitances from the sensors were recorded with a custom electronics board that transmitted data wirelessly to a smartphone. Multiple regression algorithms were implemented to predict knee angles from the strain sensors data, with the ground truth obtained from the OMC data recorded simultaneously during the experiments.
 Results
 The optimal sensor placements were above the kneecaps, orientated as the vastus medialis and the rectus femoris. Xgboost regression algorithm yielded best performance for walking with root mean square errors (RMSE) of 10.79°, 3.77°, and 2.49° for the sagittal, frontal, and transverse angles, respectively. Linear regression performed the best for knee ab/adduction with RMSEs of 8.96°, 6.33°, and 1.58° for the sagittal, frontal, and transverse angles (Fornaciari, 2023).
 Discussion/Conclusion
 The smart garment system was overall able to track the knee angle in three planes. The larger errors, compared with previous works (Gholami et al., 2019), reported for the walking and turning around movement are likely because of high variations in the movements of the participants during turning around. Additionally, the proposed system showed capability to monitor frontal and transverse angles with an average RMSE of 3.5°. The larger error values of the sagittal angles are likely because of higher range of motion in that plane. The proposed system allows for continuous and unobtrusive knee angle monitoring outside of the laboratory settings in the comfortable form factor of smart clothing.
 References
 Cuthbert, T. J., Hannigan, B. C., Roberjot, P., Shokurov, A. V., & Menon, C. (2022). HACS: Helical auxetic yarn capacitive strain sensors with sensitivity beyond the theoretical limit. Advanced materials, 35(10) Article 2209321. https://doi.org/10.1002/adma.202209321
 Fornaciari, A. (2023). Wearable technology for lower limb movement monitoring [Master’s thesis]. Politecnico di Milano.
 Gholami, M., Rezaei, A., Cuthbert, T. J., Napier, C., & Menon, C. (2019). Lower body kinematics monitoring in running using fabric-based wearable sensors and deep convolutional neural networks. Sensors, 19(23), Article 5325. https://doi.org/10.3390/s19235325

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.