Sensor Placement Strategy Research Articles

Sound Sensor Placement Strategy for Condition Monitoring of Induction Motor Bearing

Damage to the bearing elements will affect the rotation of the rotor and lead to the cessation of motor operation. Therefore, it is imperative to monitor the condition of the bearings to provide information on timely maintenance actions, improve reliability, and prevent serious damage. One of the important keys to an effective and accurate monitoring system is the placement of sensors and proper signal processing. Sound signal issued by the motor during operation capable of describing its elements’ condition. Therefore, this study aims to develop a sound sensor placement strategy appropriate for monitoring the condition of induction motor bearing components. This study was carried out on three-phase induction motors’ outer-race, inner-race, and ball-bearing sections with the signal processing method using the spectrum analysis. Furthermore, the effect of sound sensor placement on condition monitoring accuracy was determined using the One-Way Analysis of Variance (One-Way ANOVA) approach. This process tests the null hypothesis and determines whether the average of all groups is the same (H0) or different (H1). Furthermore, Tukey’s test was applied to obtain effective sound sensor placement, with voice-based condition monitoring used for effective identification. The test found that the accuracy of monitoring the bearing condition was 92.66% by placing the sound sensor at 100 cm from the motor body.

An optimal sensor placement scheme for wind flow and pressure field monitoring

Sensing of wind pressures on wind-sensitive structures and wind speeds in densely populated cities is crucial to ensure structure safety and monitoring urban wind environment. However, these wind pressure or wind speed sensors are often sparsely distributed due to their high cost, and consequently the collected wind pressure or speed information is limited. Under these circumstances, it is particularly important to place these limited number of sensors in the optimal locations with the capacity of extracting the most information. In this study, an optimal sensor placement scheme based on multi-resolution dynamic mode decomposition (mrDMD), a space post-processing function: signed distance function (SDF) and particle swarm optimization-random forest (PSO-RF) is proposed, called mrDMD-SDF-PSO-RF (mrDSPR). The effectiveness of the proposed scheme is verified using two case studies of reconstruction based on sparse sensors: wind flow field of a city block and wind pressure field of a single building. The result indicates that the proposed scheme significantly improves the efficiency of the sensors. Therefore, based on the proposed optimal sensor placement scheme, the locations of placing sensors can be well-designed for various wind engineering problems, which builds a solid foundation of super-resolution reconstruction of wind pressure on buildings and wind flow field in urban environment.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

Performance-based optimal sensor placement method for single-layer reticulated shells considering modal observability and damage identifiability

Structural health monitoring allows for the early detection of structural damage and provide valuable insight into structural performance. However, duo to numerous degrees of freedom and dense vibration modals, designing a sensor placement scheme for Single-Layer Reticulated Shells (SLRSs) is a challenging task. This paper introduces two performance indexes, namely the modal observability and damage identifiability, to quantitatively evaluate the efficiency of the sensor system. Subsequently, based on these indexes, the multi-objective optimization algorithm, as well as the performance-based design concepts, a novel performance-based optimal sensor placement (OSP) method for SLRSs is proposed. Two types of sensor placement performance curves, namely the sensor placement effect curve and the sensor placement proportion curve, are presented to determine the optimal number and placement of sensors in different directions. Numerical examples demonstrate that the proposed method can generate OSP schemes that meet various performance targets, and the performance-based OSP method is superior to the simple multi-objective OSP method. Furthermore, suggestion on selecting the total number of sensors for practical engineering applications is also provided.

Merging experimental design and structural identification around the concept of modified Constitutive Relation Error in low-frequency dynamics for enhanced structural monitoring

This paper presents a novel Optimal Sensor Placement (OSP) strategy that is dedicated to model updating problems based on the modified Constitutive Relation Error (mCRE) functional in low-frequency dynamics. The mCRE is a credible alternative to model updating functionals that stands out by searching structural parameters alongside mechanical fields as the best trade-off between all available information from measured data, without any further a priori assumption. Considering damage detection problems, due to possible discrepancies in terms of parameters sensitivity with respect to mCRE, sensor locations provided by standard OSP algorithms may be irrelevant. The proposed approach uses the concept of Information Entropy by formulating a modified Fisher information matrix, in which the sensitivity of the mCRE mechanical fields with respect to the updated parameters is involved. The approach is legitimated by the strong connection between mCRE and Bayesian inference. A proof-of-concept involving an earthquake engineering inspired academic case study, where accelerometers are positioned on a two-story frame structure subjected to random ground motion, permits to illustrate the soundness and efficiency of the proposed methodology compared to other classical OSP techniques. The influence of critical mCRE parameters is shown, as well as the benefits of taking multiple scenarios into account so as to get an OSP that is relevant for a wider range of possible damage occurrences.

A data-driven sensor placement strategy for reconstruction of mode shapes by using recurrent Gaussian process regression

Current Optimal Sensor Placement (OSP) strategies for bridges mostly rely on data from a finite element model rather than from the real structure due to high cost in placing massive sensors for data collection. For large-scale bridges, however, it is difficult to formulate a precise model and thus the OSP strategies building upon a finite element model inevitably suffer from modelling errors. Besides, the finite element model cannot account for real measurement noise. Premised on the fact that it is not expensive to make in-situ trial measurements with a few sensors on a target bridge before deploying a structural health monitoring (SHM) system on it, a data-driven OSP strategy is proposed in this study which aims at accurately reconstructing mode shapes (to facilitate vibration-based structural damage detection) by using only a few vibration sensors to be included in the SHM system. The proposed OSP strategy is also applicable for the upgrade of a long-term SHM system currently deployed on a bridge, by using historical data collected from the current SHM system. To precisely reconstruct mode shapes, a two-stage OSP strategy in terms of Recurrent Gaussian Process Regression (RGPR) is developed, and its performance is validated on a simulation model and a real bridge. In the first stage, the greedy algorithm is leveraged to temporarily deploy sensors on the structure and train accurate RGPR models using the collected data, which are used to afford spatially complete mode shape data for optimization later. Starting from a few sensors temporarily deployed on the bridge, a one-by-one sensor adding procedure is performed to configure increasing sensors until the target is achieved. In the second stage, Cuckoo Search (CS) algorithm is pursued to obtain the globally optimal sensor placement solution, from which the temporarily deployed sensors can be re-configured to the optimum positions. Both the best sensor quantity and positions are obtained by the proposed OSP strategy.

Distributed Thermal Monitoring of High-Voltage Power Lines

The purpose of this paper is to present the sensor placement strategies that currently determine the thermal monitoring of the phase conductors of high-voltage power lines. In addition to reviewing the international literature, a new sensor placement concept is presented based on a strategy centered on the following question: What are the chances of thermal overload if devices are only placed in certain tension sections? In this new concept, the number and installation location of the sensors are determined in three steps, and a new type of tension-section-ranking constant is introduced that is universal in space and time. The simulations based on this new concept show that the data-sampling frequency and the type of thermal constraint influence the number of sensors. The paper's main finding is that there are cases when only a distributed sensor placement strategy can result in safe and reliable operation. However, due to requiring a large number of sensors, this solution means additional expenses. In the last section, the paper presents different possibilities to reduce costs and introduces the concept of low-cost sensor applications. These devices can result in more flexible network operation and more reliable systems in the future.

A Computational Geometry-based Approach for Planar k -Coverage in Wireless Sensor Networks

The problem of coverage is one of the most crucial issues among the problems in the lifecycle of the development of wireless sensor networks (WSNs). It is still open and stirs as much concern in the research community in this area. The problem of k -coverage in WSNs is even more challenging. In this article, we investigate the k -coverage problem in planar (or two-dimensional) WSNs, where each point in a field of interest (FoI) is covered by at least k sensors simultaneously, where k ≥ 1. Our contribution is four-fold: First, we determine the optimal planar convex tile that maximizes the usage of the sensors’ sensing range. Then, we propose a few sensor placement strategies based on the degree of coverage k using a hexagonal tiling-based approach. In addition, we compute the sensor density (i.e., number of sensors per unit area) for each of the above sensor placement strategies. Second, we propose a generalized one using irregular hexagons, which are denoted by IRH(r/n) , where r stands for the radius of the sensors’ sensing range and n ≥ 2 is a natural number. Also, we derive the corresponding sensor density. Moreover, we prove that IRH(r/n) are capable of tiling the Euclidean plane using a mathematical induction proof. Third, we compute the relationship between the sensing range r of the sensors and their communication range R for the above sensor placement strategies. Fourth, we corroborate our analysis with simulation results.

State Estimation in Partially Observable Power Systems via Graph Signal Processing Tools.

This paper considers the problem of estimating the states in an unobservable power system, where the number of measurements is not sufficiently large for conventional state estimation. Existing methods are either based on pseudo-data that is inaccurate or depends on a large amount of data that is unavailable in current systems. This study proposes novel graph signal processing (GSP) methods to overcome the lack of information. To this end, first, the graph smoothness property of the states (i.e., voltages) is validated through empirical and theoretical analysis. Then, the regularized GSP weighted least squares (GSP-WLS) state estimator is developed by utilizing the state smoothness. In addition, a sensor placement strategy that aims to optimize the estimation performance of the GSP-WLS estimator is proposed. Simulation results on the IEEE 118-bus system show that the GSP methods reduce the estimation error magnitude by up to two orders of magnitude compared to existing methods, using only 70 sampled buses, and increase of up to 30% in the probability of bad data detection for the same probability of false alarms in unobservable systems The results conclude that the proposed methods enable an accurate state estimation, even when the system is unobservable, and significantly reduce the required measurement sensors.

Assessing Detection Efficiencies for Continuous Methane Emission Monitoring Systems at Oil and Gas Production Sites

Continuous monitoring systems, consisting of multiple fixed sensors, are increasingly being deployed at oil and gas production sites to detect methane emissions. While these monitoring systems operate continuously, their efficiency in detecting emissions will depend on meteorological conditions, sensor detection limits, the number of sensors deployed, and sensor placement strategies. This work demonstrates an approach to assess the effectiveness of continuous sensor networks in detecting infinite-duration and fixed-duration emission events. The case studies examine a single idealized source and a group of nine different sources at varying heights and locations on a single pad. Using site-specific meteorological data and dispersion modeling, the emission detection performance is characterized. For these case studies, infinite-duration emission events are detected within 1 h to multiple days, depending on the number of sensors deployed. The percentage of fixed-duration emission events that are detected ranged from less than 10% to more than 90%, depending on the number of sources, emission release height, emission event duration, and the number of sensors deployed. While these results are specific to these case studies, the analysis framework described in this work can be broadly applied in the evaluation of continuous emission monitoring network designs.

Horizontal convergence reconstruction in the longitudinal direction for shield tunnels based on conditional random field

Tunnel horizontal convergence monitoring is essential to ensure the operation safety. However, only a few representative tunnel sections are chosen for monitoring due to the cost limitation. It is difficult to capture the horizontal convergence of each tunnel ring with limited measurements. Confronted with this difficulty, the paper proposes a horizontal convergence reconstruction method based on the measurements of deployed sensors. The tunnel horizontal convergence along the longitudinal direction is seen as a one-dimensional stationary and ergodic random field. The reconstruction problem is then transformed into the generation of conditional random fields. Monte Carlo simulation is adopted to generate possible realizations and the mean of realizations is considered as the maximum likelihood reconstruction. Error analysis proves the effectiveness of the proposed reconstruction method. The proposed method is proved to be applicable in reconstructing the time-variant horizontal convergence and is verified by the monitoring results of the shield tunnel of Shanghai Metro Line 2. The effect of sensor numbers is parametrically studied, and an optimal sensor placement scheme is decided. Additional sensors placed at the deformation drastically changed location can significantly improve the performance of the proposed method.

Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance. The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’ access network. The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness. Coverage and energy usage are mostly determined by successful sensor placement strategies. Nature-inspired algorithms are the most effective solution for short sensor lifetime. The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks (WSNs’) maximum network coverage. Moreover, it identifies quantity of installed sensor nodes for the given area. Superiority of algorithm has been identified based on value of optimized energy. The first half of the paper’s literature on nature-inspired algorithms is discussed. Later six metaheuristics algorithms (Grey wolf, Ant lion, Dragonfly, Whale, Moth flame, Sine cosine optimizer) are compared for optimal coverage of WSNs. The simulation outcomes confirm that whale optimization algorithm (WOA) gives optimized Energy with improved network coverage with the least number of nodes. This comparison will be helpful for researchers who will use WSNs in their applications.

Optimal Measurement of Drone Swarm in RSS-Based Passive Localization With Region Constraints

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing optimal sensor placement strategies mainly work on the cases without any constraints on the sensor locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., maximizing the determinant of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed-form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.

A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring

Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.

Optimal Sensor Placement for Multifault Detection and Isolation in Lithium-Ion Battery Pack

The work presented in this article is motivated by a project related to the electrification of commercial aircraft. Energy storage systems (ESSs) for hybrid-electric aircraft applications require the ability to provide accurate diagnoses to insure system availability and reliability. In aerospace applications, battery packs may consist of thousands of interconnected cells and the associated electrical/electronic hardware, which brings a series of challenges for designing the battery management system (BMS). This article uses the tools of structural analysis to determine the placement of sensors that are needed by the BMS to enable monitoring and fault diagnosis at the individual cell level. First, the degree of analytical redundancy (AR) in the battery system that can be used for diagnostic strategies is determined. Then, structural models of different battery pack architectures are used to study how different measurements (current, voltage, and temperature) may improve the ability to monitor and diagnose a battery system. Possible sensor placement strategies that would enable the diagnosis of individual sensor faults, individual cell faults, and connection faults for different battery pack topologies are analyzed as well. A software-in-the-loop (SIL) framework is utilized to validate the proposed approach.

Sensor Placement Optimization for Shape Sensing of Plates and Shells Using Genetic Algorithm and Inverse Finite Element Method

This paper reports the first investigation of the inverse finite element method (iFEM) coupled with the genetic algorithm (GA) to optimize sensor placement models of plate/shell structures for their real-time and full-field deformation reconstruction. The primary goal was to reduce the number of sensors in the iFEM models while maintaining the high accuracy of the displacement results. Here, GA was combined with the four-node quadrilateral inverse-shell elements (iQS4) as the genes inherited through generations to define the optimum positions of a specified number of sensors. Initially, displacement monitoring of various plates with different boundary conditions under concentrated and distributed static/dynamic loads was conducted to investigate the performance of the coupled iFEM-GA method. One of these case studies was repeated for different initial populations and densities of sensors to evaluate their influence on the accuracy of the results. The results of the iFEM-GA algorithm indicate that an adequate number of individuals is essential to be assigned as the initial population during the optimization process to ensure diversity for the reproduction of the optimized sensor placement models and prevent the local optimum. In addition, practical optimization constraints were applied for each plate case study to demonstrate the realistic applicability of the implemented method by placing the available sensors at feasible sites. The iFEM-GA method's capability in structural dynamics was also investigated by shape sensing the plate subjected to different dynamic loadings. Furthermore, a clamped stiffened plate and a curved shell were also considered to assess the applicability of the proposed method for the shape sensing of complex structures. Remarkably, the outcomes of the iFEM-GA approach with the reduced number of sensors agreed well with those of the full-sensor counterpart for all of the plate/shell case studies. Hence, this study reveals the superior performance of the iFEM-GA method as a viable sensor placement strategy for the accurate shape sensing of engineering structures with only a few sensors.

A Framework for Optimal Sensor Placement to Support Structural Health Monitoring

Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal.

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. In this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

Multi-objective and risk-based optimal sensor placement for leak detection in a water distribution system

The optimal sensor placement for leak detection in a water supply network has been studied extensively. The previous studies usually assume that each node of the water distribution network has the same importance. However, this assumption does not consider the difference in the impact when a leak occurs at the different nodes on the water distribution network. When the number of sensors is limited, the detection of only leakage events that can cause serious consequences is usually prioritized. This study proposes a multi-objective optimization method for sensor placement while taking into consideration the differences in the risk of leakage for the different nodes. First, five types of risk-based leakage functions are obtained based on the impact on the water distribution network when a leakage occurs at each node. The pressure at the node drops, which causes the water demand and pipe pressure to decrease while the flow rate increases. The multi-objective optimization method is then used to obtain the Pareto front according to the various risk-based leakage functions. Finally, the multi-criteria decision analysis method is used to rank and cluster the Pareto fronts, the optimal solution for each cluster is obtained, and these solutions are compared and analyzed. This study contains a case study that is based on the example water distribution network model C-town. When the number of sensors is limited, a more reasonable optimal sensor placement scheme can be obtained by considering the different risk-based leakage functions of the nodes, with a smaller impact on the water distribution network when a leakage occurs. • A multi-objective optimization sensor placement for leak detection in water distribution system. • Five types of risk-based leakage functions are obtained based on the impact of each leakage. • The impacts of the leakage occurring at each node of the pipe network are compared.

Evaluating the Robustness of Water Quality Sensor Placement Strategies of Water Distribution Systems Considering Possible Sensor Failures and System Changes

An early contamination warning system with deployed water quality sensors is often used to enhance the safety of a water distribution system (WDS). While algorithms have been developed to select an optimal water quality sensor placement strategy (WQSPS) for WDSs, many of them do not account for the influences caused by future uncertainties, such as sensor failures and system changes (e.g., demand variations and configuration/expansion changes in the WDS). To this end, this paper proposes a comprehensive framework to evaluate the robustness of WQSPSs to these possible uncertainties. This is achieved by considering five different performance objectives of WQSPSs as well as possible future demand and typology variations of WDSs under a wide range of sensor failure scenarios. More specifically, an optimization problem is formulated to evaluate the robustness of the WQSPSs, in which an evolutionary-based optimization approach coupled with an efficient data-archive method is used to solve this optimization problem. The framework is demonstrated on two real-world WDSs in China. The results show that: (1) the WQSPS’s robustness can be highly dependent on the performance objectives considered, implying that an appropriate objective needs to be carefully selected for each case driven by practical needs, (2) the WDS’s demand and configuration changes can have a significant influence on the WQSPS’s robustness, in which the solution with more sensors in or close to the affected area is likely to better cope with these system changes, and (3) the proposed framework enables critical sensors to be identified, which can then be targeted for prioritizing maintenance actions.