Optimal Sensor Location Research Articles

Optimal location and geometry of sensors and actuators for active vibration control of smart composite beams

ABSTRACT This work investigates the problem of optimal placement and geometry of piezoelectric sensors and actuators (S/A) to improve active vibration control performance of smart laminated beam. Theoretical formulation is based on efficient layer-wise finite element model along with an optimal control theory. Control theory is implemented in state space format considering a reduced order model. A laminated beam with collocated piezoelectric S/A placed at the top and bottom surfaces has been considered. A series of simulation based on Taguchi design of experiment has been carried out to determine the optimal placement and geometry of the S/A to achieve best control performance that is maximum modal active damping ratio /minimum settling time with minimum actuator voltage. A multi-criteria optimisation has been performed using Proximity Indexed Value (PIV) integrated with entropy methods. Further, the analysis of mean (ANOM) and analysis of variance (ANOVA) are performed to identify the most significant input parameter based on their percentage contribution. For the smart cantilever beam considered in the analysis, the location of the S/A is the most significant having maximum percentage contribution (35.69%) followed by its length (33.04%) and thickness (24.55%).

Multiple load path damage detection with optical fiber Bragg grating sensors

In this article, a new damage indicator is presented that can detect a (partial) load path failure for a multiple load path structure, based on variable amplitude strain response measurements by fiber optic Bragg grating sensors. Many (aircraft) structures have multiple load paths where after a (partial) failure of a load path the remaining structure can carry the limit load without catastrophic failure or too severe impact on the operational characteristics of the whole structure, until the structure is repaired, replaced, or modified. The damage indicator is defined as a ratio of the strain response summation of the current strain time variation measured at fiber Bragg grating (FBG) sensors and a reference strain time variation measured at the same FBGs. For application in real structures with varying loads and environmental conditions, it should be insensitive for the load time variation as well as temperature. The FBG strain response due to a load variation can be easily computed by means of a finite element analyses. In this way, an optimal number, location and orientation of strain sensors can be derived for complex structures. A test was performed on a box-shaped structure representative of a typical aircraft structure to validate the damage indicator, showing that the damage indicator is able to detect a damaged load path at an early stage of failure with few sensors per load path. From finite element analyses, the damage size can even be determined from the damage indicator value for a known damage scenario, making it a level 3 structure health monitoring system.

Sealant Delamination Detection of Structural Sealant Glazing Systems Based on Driving-Point Accelerance

The structural sealant glazing system is widely used in glass curtain wall worldwide. However, due to the aging of the sealants, cracking may form along the sides of the glass. If the panel of curtain wall arises, delamination may occur and induce failure or loss of the system. In order to detect the delamination of the sealant, in this paper, a new vibration-based damage identification method is proposed by using the difference between the predamage and postdamage driving-point accelerance (DA). This detection method would require only one acceleration sensor on a glass panel and a rubber hammer, which operates very conveniently. When the rubber hammer taps the glass panel near the acceleration sensor, the glass curtain wall panel would generate acceleration response which can be measured by the acceleration sensor. The measured acceleration responses will be used to calculate the DA and lead to a new delamination index, called relative accumulative difference of DA, which will give indications on the potential delamination of the sealant. In addition, the influence of the acceleration sensor installation position was analysed by the finite element method, and the optimal sensor location was determined to be at the intersection of the long-side quarter and the short-side quarter. Nine cases on various delamination severities were identified by the new method, as well as the natural frequency reduction and modal assurance criterion. The laboratory experiments showed that the relative accumulative difference of DA is extremely sensitive to sealant delamination. Even if delamination severity was only 6.39%, the relative accumulative difference of DA would be larger than 18%. As the delamination of the sealant progresses, the relative accumulative difference of DA increases, resulting in an effective detection method for sealant delamination of the structural sealant glazing system. The fundamental frequency reduction is suitable to identify relatively large delamination, and the modal assurance criterion of higher modes is also sensitive to sealant delamination.

Strategy for sensor number determination and placement optimization with incomplete information based on interval possibility model and clustering avoidance distribution index

A strategy for sensor number determination and placement optimization for structural health monitoring is proposed based on an interval possibility model and clustering avoidance distribution index. The performance of sensor configuration with optimum sensor number and less redundant information is primarily improved by considering two types of incomplete information simultaneously, namely uncertainty and redundancy. This study regards uncertainty as a non-probabilistic interval number to avoid the deficiency caused by the insufficient statistical distribution of uncertain structural parameters. As long as the bound of uncertainty is obtained, then a strategy for the sensor number can be determined using the interval possibility model and effective independence method. A novel redundant information elimination model is defined to reduce the redundant information caused by sensor clustering configuration, which considers the near neighbor distance, sensor configuration region and sensor number synchronously. Based on these two methods to address incomplete information in the sensor configuration field, the strategy for sensor placement optimization constitutes a novel combined fitness function, which comprises the effective independence method and clustering avoidance distribution index. Using the normalization and weighting factor, the combined fitness function is made more competitive by eliminating the order difference between the two objectives. Therefore, the best sensor number is suggested and the corresponding optimum sensor locations are achieved simultaneously using the genetic algorithm and the proposed strategy for sensor number and placement. The performance of sensor configuration and redundant information with incomplete information is balanced by the proposed strategy. Finally, the effectiveness of the proposed strategy and method is verified through three engineering numerical examples.

Sensor Location Strategy and Scaling Rate Inference for Origin-Destination Demand Estimation

The goal of sample-data-based origin-destination (O-D) demand estimation is to aggregate the location data into the traffic network. Inevitably, the scaling rate, which expands the estimated O-D demands to the population level, should be generated. In this paper, a two-stage optimization model is explored for determining the sensor location and stochastic scaling rate. In the first stage, a sensor deployment model identifies the optimal sensor location strategy by minimizing the variability of the scaling rate under a budget constraint. In the second stage, a Bayesian-based scaling rate inference model leverages the prior information and the data that were observed at the identified sensor locations to derive the stochastic scaling rate. The Bayesian-based scaling rate inference model is a bilevel program, which seeks the maximum a posteriori (MAP) scaling rate conditioned by the observed link flows in the upper level and optimizes the stochastic user equilibrium (SUE) in the lower level. To reflect the interactive relationships between the sensor location and the stochastic scaling rate inference, an integrating model is built. A sequential identifying sensor location algorithm that avoids matrix inversions was proposed to solve the sensor deployment model, and an iterative solution algorithm was developed to solve the Bayesian-based scaling rate inference model. The results from numerical experiments demonstrate that the sensor deployment model could provide the most reliable scheme of sensor locations, thereby contributing to the reliable estimation of the stochastic scaling rate. The results also demonstrate that both the endogenous information (prior information on the scaling rate) and exogenous factors (link flows) can facilitate a more accurate scaling rate inference.

Structural health monitoring of an offshore wind turbine tower using iFEM methodology

With the increasing popularity of wind energy, offshore wind turbines (OWTs) are currently experiencing rapid development. The tower is one of the most significant components of the OWT. However, the tower will not only stand its own weight and weight of the top structure, but also be surrounded by harsh wave and wind loading conditions. Therefore, it is necessary to apply a structural health monitoring (SHM) system to monitor the health condition of the OWT towers in real-time. In this study, inverse Finite Element Method (iFEM) is applied to monitor the tower of an OWT under both static and dynamic loading conditions. The total displacements and von Mises stresses obtained from iFEM analysis are compared against reference results and optimum sensor locations are determined.

Closed-form H-infinity optimal control for a class of infinite-dimensional systems

H-infinity optimal control and estimation are addressed for a class of systems governed by partial differential equations with bounded input and output operators. Diffusion equations are an important example in this class. Explicit formulas for the optimal state feedback controller as well as the optimal state estimator are given. Unlike traditional methods for H-infinity synthesis, no iteration is needed to obtain the optimal solution. Moreover, the optimal performance for both the state feedback and state estimation problems are explicitly calculated. This is shown to be useful for problems of H-infinity optimal actuator and sensor location. Furthermore, the results can be used in testing and bench-marking of general purpose algorithms for H-infinity synthesis. The results also apply to finite-dimensional systems.

Damage‐sensitive impedance sensor placement on multi‐strand anchorage based on local stress variation analysis

An important issue in impedance-based damage monitoring is to deploy sensors in proper positions in which damage-sensitive impedance responses can be captured effectively. In this study, a full-scale multi-strand anchorage is analyzed to determine optimal locations of piezoelectric sensors for impedance-based monitoring of locally damaged strands. First, stress variations of the multi-strand anchorage are experimentally measured to estimate the anchorage behavior under the effect of locally damaged strands. Strain signals are examined for axial, circumferential, and radial stress components under the variation of prestress forces. Second, a finite element analysis is made on the multi-strand anchorage to back up the experimental findings. Third, a damage-sensitive structural model is interpreted for the local strand breakage. Finally, impedance responses sensitive to local strand breakage are experimentally examined for a few scenarios simulated in the anchorage system. PZT (lead zirconate titanate) sensors deployed on the anchor head and the bearing plate are evaluated to comparatively determine ideal regions of interest for impedance monitoring. The results show that the greater stress variation yields the greater variations in impedance responses and the near-top and near-anchor heads are ideal regions of interest for damage-sensitive impedance monitoring.

Improved multi-objective sensor optimization method for structural damage identification based on genetic algorithm

An improved multi-objective sensor location optimization method based on genetic algorithm is proposed. According to the analytic relationship between structural response, mode shapes and damage quantities, the sensitivity matrix of structural dynamic response containing both structural modal information and damage information is firstly constructed, and then the objective function of multi-objective sensor location optimization method is established according to the minimum identification error criterion and the principle of ill- posedness in the inverse problem. In addition, a multi-objective genetic algorithm, NSGA-II, is used to solve the optimal location of measurement points. Finally, a simply supported box girder bridge is studied for validating the proposed method. Numerical simulations show that the improved optimization method based on genetic algorithm can achieve the optimization goal better, and the measurement points optimized by the improved method can get better damage identification results at the same level of measurement noise.

Pre‐ and post‐earthquake regional loss assessment using deep learning

SummaryAs urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community‐level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre‐ and post‐earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre‐earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near‐real‐time post‐earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area‐wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre‐ and post‐earthquake regional loss assessments.

Optimal Sensor Placement for Laminated Composite and Steel Cantilever Beams by the Effective Independence Method

In modal testing, the quality of vibration signals and clarity of peak points to extract the natural frequencies, corresponding mode shapes and damping ratio depend on the number and location of the sensors. High measurement costs are required for structural identification and long-term structural health monitoring in large structures, which require a high number of sensors. Therefore, the minimum number of sensors should be placed at appropriate locations on the system during experimental measurements to ensure both information of sufficient quality and cost reductions. The aim of this study is to perform cost-efficient non-destructive modal tests for a laminated composite and steel cantilever beams using an optimal sensor placement approach. Finite element models of the sample beams are constituted in ANSYS® software to determine the initial candidate set of sensor locations. Then, ambient vibration tests are conducted. Based on the experimental modal amplitudes and mode shapes, optimal sensor locations are determined using the effective independence method, and measurements are repeated. The study shows that there is good agreement between the natural frequencies and mode shapes obtained from the initial measurements and those obtained using the limited number of sensors.

Data‐driven sensor placement for state reconstruction via POD analysis

In this study, a data-driven framework to determine the optimal sensor locations for power system oscillation monitoring and state reconstruction is proposed. In this approach, candidate sensor locations are selected sequentially using proper orthogonal decomposition (POD) analysis of a reduced-order binary measurement matrix. Using criteria inferred from the spatial structure of the POD modes, sensor locations having large magnitudes and small cross couplings between modes at the candidate locations are selected from the measurement matrix. The method can be used to reconstruct global system dynamics using a few sensor measurements as well as for the prognosis of power system oscillatory behaviour. The developed procedures are applied to the IEEE 39-bus test system and to a realistic 5449-bus model of a large power system.

Locating Sensors in Complex Engineering Systems for Fault Isolation Using Population-Based Incremental Learning

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged.

An Integrated Optimization Enabled Sensor Deployment Model in Wireless Sensor Network

ABSTRACT Target coverage (TCOV) and network connectivity (NCON) are the most basic problems affecting robust data communication and environmental sensing in a wireless sensor network (WSN) application. This article proposes an intelligent Context Aware Sensor Network (CASN) for the process of sensor deployment in WSNs. Accordingly, the process is sub-divided into two phases. In the initial phase, optimal TCOV is performed; whereas, in the second phase, the proposed algorithm establishes NCON among the sensors. The objective model that meets both TCOV and NCON is evaluated as the minimization problem. This problem is solved by a new method that hybridizes the Artificial Bee Colony (ABC) algorithm and the Whale Optimization Algorithm (WOA) together, which is known as the Onlooker Probability-based WOA (OP-WOA) for the determination of optimal sensor locations. In addition, the adopted OP-WOA model is compared with the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), the ABC algorithm, Differential Evolution (DE), FireFly (FF), the WOA, and the Evolutionary Algorithm (EA)-based TCOV and NCON models. Finally, the results attained from the execution demonstrate the enhanced performance of the implemented OP-WOA technique.

Dynamic model reduction and optimal sensor placement for agro-hydrological systems

One of the essential aspects of developing advanced closed-loop irrigation is the estimation of soil moisture from a limited number of available sensors. One of the challenges is to find the optimal location of the sensors in a large heterogeneous field. In this work, we propose a method to find the optimal location of sensors in the presence of heterogeneous soil and non-uniform inputs. The key steps include dynamic order model reduction, minimum sensor selection, optimal sensor placement, and state estimation. The proposed method is applied to a three-dimensional field through simulations, and satisfying model reduction and state estimation results are obtained.

Sensor network design for smart manufacturing – Application on precision machining

Energy consumption in a manufacturing facility comprises direct energy used in the manufacturing operations and indirect energy consumed by activities to maintain proper equipment conditions (e.g., heating and cooling). Reducing the energy consumption in a manufacturing facility requires sensors to monitor the energy usage patterns (“energy profiles”) and a concomitant data analytics process for correlating them with the activities being performed. This work explores the design and integration of optimal sensor networks for measuring and identifying the context of energy usage in manufacturing processes. This information is useful in production planning and scheduling to optimize energy usage and reduce energy cost. We explore a system-level representation of precision machining for optimal sensor locations and types that allow the monitoring of energy consumption. This is accomplished through maximization of a measure of the information matrix, subject to constraints on the cost of sensors. First, a system-level model is presented for predicting energy consumption in precision machining. A formulation is then presented for the selection of sensors and the operating mode for maintenance tests in manufacturing. The sensor network design is cast as a mixed-integer non-linear program that selects possible sensors based on their contribution to information gain with respect to energy consumption and their impact on equipment cost. For this purpose, we explore the sensitivity of the machining process with respect to admissible inputs at different system fault scenarios.

Graph-based determination of structural controllability and observability for pressure and temperature dynamics during steam-assisted gravity drainage operation

Steam assisted gravity drainage (SAGD), which is used for the in-situ extraction and recovery of oil sands bitumen, is represented by a distributed parameter system (DPS). The problem of sensor placement and the control of steam chamber growth and oil production, respectively, require analysis of the observability and controllability of the system. In this type of system, parametric sensitivity is traditionally used in lieu of observability, and controllability has not been explored rigorously. In this work, we analyze the pressure and temperature fields of a SAGD model based on detailed reservoir simulations and present a data-driven technique to assess the structural controllability and observability of the system, with a view to determine optimal locations of actuators and sensors. An agglomerative hierarchical clustering technique is used to obtain a spanning tree of the clusters which is partitioned based on an objective function to arrive at a set of spatially contiguous clusters that display similar pressure/temperature dynamics. A Granger causality measure is used to create the linkage amongst the clusters to build a digraph model of the data. The driver nodes of the graph identify locations for actuation which provide full control over the graph, and the root strongly connected components indicate sensor locations which ensure structural observability over the entire graph. We demonstrate the method using data generated from SAGD simulations using the CMG-STARS simulator, identify the sensor and actuator locations required for complete structural observability and controllability of the system, and also provide a method of assessment of partial actuation and in-sensor ranges.

Almost always observable hybrid systems

For a class of hybrid systems we introduce and characterize the almost always observability property, i.e. the possibility of reconstructing the current continuous component of the state, from the observed output, for almost all switching times. The optimal location of additional sensors that make the system almost always observable is investigated.

Optimal sensor placement for monitoring and controlling greenhouse internal environments

In large greenhouses with information and communication technology capability, environmental conditions can be measured and communicated to control the internal environment. However, in such greenhouses, it is difficult to control the internal environment uniformly and appropriately. Additionally, there is uncertainty regarding whether the data measured at a particular location accurately represents the entire greenhouse environment. Furthermore, the locations of sensors are usually determined based on the experience of growers and the greenhouse designers. To accurately measure the internal environment of a greenhouse, it is necessary to properly select the installation locations of the sensors. The objective of this study was to determine the optimal sensor placement for monitoring and controlling the internal environment of a greenhouse. The study greenhouse was an eight-span plastic greenhouse growing Irwin mango crops. Air temperature data measured at nine locations in the greenhouse were used. All of the possible combinations of monitoring locations were evaluated, and optimal sensor placements were selected according to the number of sensors. The optimisation was conducted using two methods: error-based sensor placement and entropy-based sensor placement. Using the former approach, sensor locations for which the monitored data were close to the reference value, i.e. the average data of all the measurement locations, were selected. Using the latter approach, sensor locations influenced by the external weather conditions resulting in poor environmental control were selected. Using these methods, optimal sensor locations for representing the entire environment of the facility and for detecting areas with significant air temperature variations were determined.

Asymmetric Dual Sensor Latency in Distributed Control

Considerable geographical separation between various phasor measurement unit sensor suites used in the wide-area damping control architecture results in time delays with varying steady-state and stochastic components. The consequences of asymmetric latent sensors used in a standard feedback configuration are presented, indicating that asymmetry of the delay provides not only a previously suspected modification to the argument of the loop transmission function, but also a potentially substantial impact on its modulus. Under particular delay configurations, the controller may experience the introduction of varying crossovers in the loop transmission, resulting in a shifting of control bandwidth and previously unconsidered (in)stability characteristics (e.g., alteration of relative stability margins). With consideration to the theoretical results provided, the optimal sensor location based on delay characteristics is provided. Finally, results of the study indicate that it is most frequently best practice to act on the most current sample in the feedback channel, rather than wait for precise time alignment of the delayed sensor.