Sensor Layout Research Articles

Vertical Displacement Measurement of Tunnel Structures Based on Long-Gauge Fiber Bragg Grating Strain Sensing

Displacement monitoring systems play a crucial role in ensuring the safety of tunnels. Existing sensing technologies and analysis methods may be insufficient for monitoring tunnel displacement, particularly vertical displacement, due to the harshness of long-term monitoring conditions and the intricacies of structural characteristics. A long-gauge fiber Bragg grating (FBG) sensor can be used to obtain macro- and micro-level information and be connected in series for area sensing. In this study, a novel method was developed which utilizes long-gauge strain sensors to monitor the vertical displacement of a tunnel. This method employs a combination of mechanical analysis and monitoring data to accurately estimate the vertical displacement of the structure from the measured coupled strain. Several key aspects of the proposed method for identifying vertical displacement were investigated, including establishing a separation model of coupled strain on the cross-section, deriving the theory for vertical displacement identification, and determining the sensor layout of the tunnel. A series of simulation tests of a tunnel with a three-hole frame structure confirmed the efficiency and robustness of the proposed method, even when subjected to various loading conditions, noise levels, and sensor layouts. The results of this work may provide valuable insights and practical guidance for the effective and continuous displacement measurement of tunnels, ensuring their structural integrity and operational safety.

Bridge-bearing disengagement identification based on flexibility matrix diagonal matrix change rate: an indoor physical simulation experiment

The disengagement of bridge bearings is a pervasive issue encountered in the realm of bridges, which can potentially lead to changes in operational circumstances, diminished longevity, and compromised traffic safety. The current methods employed for detecting such disconnections primarily rely on force sensors, cameras, and acceleration sensors. However, their practical implementation on-site and effectiveness in accurately identifying disengagement require enhancement. To address the challenges associated with the installation and layout of conventional contact sensors, as well as the potential introduction of additional mass, a sophisticated “bridge-bearing disconnection detection system” has been devised. This innovative system is based on laser Doppler vibrometer technology, which eliminates the need for physical contact. The feasibility of employing non-contact laser Doppler vibration measurement technology in the detection of bridge-bearing disconnection has been successfully verified within the framework of this study. Furthermore, a comprehensive analysis of the sensitivity of key dynamic parameters, specifically natural frequencies and vibration modes, to bridge-bearing disengagement has been conducted. The verification process included evaluating the identification effectiveness of regularized combined absolute changes in vibration modes and flexibility matrix diagonal matrix change rate (FDMCR) under diverse working conditions simulating complete disconnection. This assessment involved using both finite element analysis and empirical measurements. The findings unequivocally demonstrate that the disconnection of bridge bearings results in a reduction in the natural frequencies for each mode order, with an observed cumulative effect. In addition, it is noteworthy that the vibration mode indices typically exhibit greater sensitivity toward the disconnection of outer bearings. By contrast, FDMCR demonstrates commendable positioning capabilities and exceptional noise resistance in identifying bridge-bearing disengagement. The empirical insights gleaned from these research findings hold significant value in terms of on-site identification of bridge-bearing disengagement, ultimately contributing to the preservation of bridges’ long-term operational integrity.

Optimal sensor placement for leak location in water distribution networks based on EGAE clustering algorithm

In the water supply network system, the placement of pressure sensors is crucial to the optimization of the monitoring effect of the system running condition. Most of the existing studies select the placement of sensors according to the hydraulic state of the water distribution network, but do not fully consider and analyze the connectivity between nodes and the topology of the water distribution network. Aiming at the shortcomings of the existing pressure monitoring point layout methods, this paper proposes a pressure sensor layout method based on the Embedding Graph Autoencoder (EGAE) clustering method. Firstly, the EGAE deep clustering algorithm is used to cluster the network nodes based on the topology structure and hydraulic characteristics of the network. According to the clustering results, the network nodes are divided into different regions. Then, by calculating the correlation between nodes in the region, the representative nodes of each region are selected and set as sensor layout nodes. By comparing and analyzing the performance of experiments with other schemes, the results show that the EGAE deep clustering network can effectively integrate the topological structure characteristics and hydraulic characteristics of the water distribution network. The sensor nodes laid out in the scheme proposed in this paper can effectively carry out accurate leakage positioning in subsequent positioning models, and have higher leakage pipe segment identification accuracy and smaller precise positioning errors compared with other schemes.

A robust design strategy for active control of scattered sound based on virtual sensing.

Combining virtual sensing (VS) with scattered sound control enables active acoustic cloaking when there are limitations in sensor configurations. The remote microphone method and additional filter method (AFM) are two common VS methods, and both can be divided into the training and control stages; the consistency of the environments in these two stages is essential for the control system. This paper investigates the effects of uncertainties in the incidence angle of the detection wave on these two VS-based scattered sound control methods. Following the analysis, we propose a robust design strategy based on the optimal layout of physical sensors, and the placement scheme is chosen by minimax optimization. The feasibility of the proposed strategy is verified by numerical simulations of a finite-length cylindrical scattering model. The results demonstrate that the proposed strategy can effectively reduce the degradation of system performance over the examined range of variations in the detection waves. In particular, the AFM-based method, combined with optimal placement, shows a remarkable improvement in robustness. It improves the worst noise reduction by approximately 14.5 and 10.8 dB on average, respectively, compared with the uniform placement and the direct control method based on the Wiener solution.

Rotating blade vibration parameter identification based on genetic algorithm

In this paper, aiming at the identification of blade vibration parameters such as constant speed synchronization, constant speed asynchronous, and variable speed synchronization, the simulation platform Simulink is used to model the blade vibration system, and the blade tip timing vibration measurement system model is constructed. A method of blade vibration parameter identification based on genetic algorithm is proposed, and numerical simulation and experimental verification are carried out. The results show that the parameter identification of blade vibration by genetic algorithm has high accuracy and strong anti-noise interference ability. The influence of key parameters on the identification of blade vibration parameters is studied. For the constant speed synchronous and constant speed asynchronous vibration of the blade, the angle between the sensors should not be an integral multiple of 2π as far as possible, and the larger DR (Distribution Range) value should be guaranteed. The higher the frequency doubling of blade vibration, the more sensors are needed. For the variable speed synchronous vibration of the blade, the frequency doubling is greater than the influence of the sensor layout on the parameter identification results, but the number of sensors is too small, which will seriously affect the identification accuracy of the frequency doubling. Aiming at the blade vibration test, a blade vibration tester is designed. The blade variable speed synchronous vibration test is carried out by using the strain gauge method and the tip timing method. The measurement results of the strain gauge method are basically consistent with the measurement results based on the genetic algorithm and the tip timing.

A Large-Scale Sensor Layout Optimization Algorithm for Improving the Accuracy of Inverse Finite Element Method.

The inverse finite element method (iFEM) based on fiber grating sensors has been demonstrated as a shape sensing method for health monitoring of large and complex engineering structures. However, the existing optimization algorithms cause the local optima and low computational efficiency for high-dimensional strain sensor layout optimization problems of complex antenna truss models. This paper proposes the improved adaptive large-scale cooperative coevolution (IALSCC) algorithm to obtain the strain sensors deployment on iFEM, and the method includes the initialization strategy, adaptive region partitioning strategy, and gbest selection and particle updating strategies, enhancing the reconstruction accuracy of iFEM for antenna truss structure and algorithm efficiency. The strain sensors optimization deployment on the antenna truss model for different postures is achieved, and the numerical results show that the optimization algorithm IALSCC proposed in this paper can well handle the high-dimensional sensor layout optimization problem.

Dynamic range optimization of optoelectronic systems based on quantum mathematical models

A photodiode is a small and low-cost photosensitive sensor that can measure the solar vector through at least three combinations, making it a high-precision solar sensor. How to select the number of photodiodes and determine their layout is a challenge in order to solve the solar vector in any direction in a 360° field of view space as much as possible, the author first discretizes the equal surface area of the 360° field of view space, transforming the infinite sensor layout optimization problem into a finite combinatorial optimization (NP problem is difficult). Then, by establishing multi-objective optimization functions for coverage and uniformity, and combining quantum genetic algorithm to solve the optimal solution. The experimental analysis of the layout effects of different numbers of photodiodes and field of view ranges provides a theoretical basis for optimizing the layout of multiple photodiodes. The experimental results show that selecting 12 to 14 photodiodes can basically achieve a layout without coverage risk and uniform risk.

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

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

Development of a sensor layout for a dynamic indentation device

Issues related to the design of a dynamic indentation sensor are presented in this paper. The use of a magnetic induction method for recording the motion parameters of a striker with a differential circuit of inductors is proposed. A prototype of the primary transducer and a sensor model with a new mechanism for the striker reset system are developed.

Spatial weighted graph-driven fault diagnosis of complex process industry considering technological process flow

Each chemical process industry system possesses unique process knowledge, which serves as a representation of the system’s state. As graph-theory based methods are capable of embedding process knowledge, they have become increasingly crucial in the field of process industry diagnosis. The fault representation ability of the diagnosis model is directly associated with the quality of the graph. Unfortunately, simple fully connected graphs fail to strengthen the internal connections within the same process but weaken the interactive connections between different processes. Moreover, each node in the graph is considered equally important, making it impossible to prioritize crucial system monitoring indicators. To address the above shortcomings, this paper presents a spatial weighted graph (SWG)-driven fault diagnosis method of complex process industry considering technological process flow. Initially, the physical space sensor layout of the technological process flow is mapped into the spatial graph structure, where each sensor is regarded as a node and these nodes are connected by the k nearest neighbor algorithm. Subsequently, according to the mechanism knowledge, the sensors in the process are divided into different importance categories and weight coefficients are assigned to their nodes. The similarities between these weighted nodes are calculated, and the resulting edge information are used to construct the SWGs. Finally, the SWGs are input to a graph convolutional network, facilitating fault representation learning for fault diagnosis of complex process industry. Validation experiments are conducted using public industrial datasets, and the results demonstrate that the proposed method can effectively integrate the process knowledge to improve the fault diagnosis accuracy of the model.

Joint state–parameter estimation for structures with seismic isolation and damping systems: Theoretical observability and experimental verification

The objective of this study is to examine the feasibility of estimating the parameters of various devices in seismic control systems and evaluating the maximum responses of the quantities of interest. The observability condition was applied to determine the sensor layout and modeling assumptions of the structure for the joint estimation problem and to eliminate unsuitable layouts. To estimate the parameters of multiple members at the isolation layer, including laminated rubber bearings, oil dampers, and elastic sliding bearings, and to evaluate the maximum response, an adaptive unscented Kalman filter was utilized. Moreover, the study investigated the influence of partially unmeasured inputs on the observability of the established system through a case study involving the base layer of the building model colliding with the retaining walls. Experimental data from a full-scale shaking table test on a seismic isolation building was used as an example for validation purposes. The results demonstrated that even under varying initial estimates, varying sensor layouts, and the occurrence of unmeasured impact forces, the parameters and maximum responses of interest can be estimated with acceptable accuracy when the observability condition is met.

Comparison of printing techniques for the fabrication of flexible carbon nanotube-based ammonia chemiresistive gas sensors

Even though a plethora of printing technologies are currently available and their potential for the fabrication of low-cost and flexible sensors has been widely investigated, systematically based, and statistically sustained comparative studies are missing in the literature. In this work, we compare screen, inkjet, and dispense printing for the fabrication of carbon nanotube (CNT)-based ammonia (NH3) chemiresistive flexible gas sensors for the first time. Moreover, we report the first CNT-based gas sensor fabricated via Voltera printer. The devices were made of a thin layer of spray-coated CNTs and printed silver-based interdigitated electrodes. To draw a thoughtful comparison the same sensor layout, materials, and fabrication flow were used. The device morphological features were acquired through microscopic, atomic force microscope, and 3D images; additionally, the response to NH3 as well as the printing process characteristics for each technique was analyzed. From 300 µm nominal spacing between lines, we obtained a decrease of 25%, 13%, and 5% on the printed spacings with dispense, screen, and inkjet printing, respectively. At 100 ppm of NH3, a maximum response of 33%, 31%, and 27% with the dispense-, inkjet-, and screen-printed sensors were found, respectively. Statistical differences were observed between the mean values on the NH3 response of dispense- compared to the inkjet- and screen-printed sensors, which in effect showed the highest response in the Tukey test. This demonstrated that the fabrication technique employed can induce a different response mainly driven by the printed outcomes. Following a holistic approach that includes the sensor response, the application, the market perspective, and the process versatility, we suggest screen printing as the most suitable method for CNT-based NH3 gas sensor fabrication.

Fast reconstruction of indoor temperature field for large-space building based on limited sensors: An experimental study

Accurately knowing the spatiotemporal distribution of indoor environmental parameters is important for indoor thermal comfort adjusting and building energy saving. Due to the significant distributed parameter characteristics of the building thermal environment, this task is difficult, especially for large-space buildings with the features of multi-input and multi-output. For a real large-space building, the reduced-dimensional model of indoor temperature field is rebuilt based on proper orthogonal decomposition (POD) and an offline-online strategy. POD technology is used to extract environmental features from steady / dynamic experimental data. Based on the solved POD modes, the optimal layout of temperature sensors is obtained by a heuristic reasoning method. By combining linear stochastic estimation (LSE) method together with the selected sensors, an offline-online strategy is proposed for rapid reconstruction and prediction of the indoor temperature fields. Digital interpolation is applied for soft sensing outside the original sampling locations. An experimental study for a large-space cafeteria with sixteen indoor air conditioning units is conducted. A total of 72 sensors are placed to collect indoor 3D temperature field data offline. Results show that using only six sensors of them, the large-space indoor temperature field could be accurately estimated online with the steady-state error of 0.35 (average RMSE) and dynamic-state errors of 0.0525 / 0.0540 (heating / cooling, average RMSE).

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.

A Novel Tandem Differential Edge Sensor Layout for Segmented Mirror Telescopes.

The performance of an active control system, crucial for the co-phase maintenance of segmented mirrors, is closely related to the spatial layout of sensors and actuators. This article compares two types of edge sensor layouts, vertical and horizontal, and proposes a novel tandem differential sensor layout that saves layout space and reduces the number of positioning references. The control performance of this scheme is analyzed in terms of error propagation, mode representation, and the scalable construction of the control matrix. Finally, we constructed a tandem differential-based sensor detection system to examine the performance of edge sensors and the effect of laboratory environmental variables on sensor measurements. Simulations and experiments demonstrate that this scheme has the same ability to fully characterize actuator modification modes as the Keck edge sensor layout. Although the total error multiplier is slightly larger than the latter, it has fewer scalable control matrix types and stronger spatial and segmental shape adaptation capabilities. Actual measurements show that the sensor's own noise in a tandem differential layout is less than 20 nm, which meets the requirements for future segmented co-phase maintenance. This layout type can potentially be applied to future small and medium-sized segmented splices.

An information-entropy-inspired optimal layout method for structural stress sensors of supersized open caissons

The layout of sensors directly influences the effectiveness of structural stress monitoring of open caissons during the sinking process. In this study, an optimal layout method for structural stress sensors is proposed inspired by information entropy. The information gains of different sinking states from different layout locations of structural stress sensors are calculated, and the important layout locations are identified through the calculation of information gains. According to these important layout locations, the optimal sensor layout scheme is obtained. The proposed method was validated via an engineering case study of the Changtai Yangtze River Bridge Project. The results show that the proposed method successfully determined the optimal sensor layout scheme and that the number of required structural stress sensors decreases by 51.5% compared with the number of structural stress sensors deployed in the engineering case. With all R2 values greater than 0.96, the optimal scheme has high prediction accuracy for three sinking states: sinking speed, transverse height difference and longitudinal height difference. In addition, the spatial distribution of the important layout locations was identified. This study is meaningful for obtaining high-quality monitoring data of structural stress to both ensure the steady sinking of open caissons and reduce monitoring costs.

Theoretical and experimental investigation of guided wave temperature compensation for composite structures with different thicknesses

This study presents a novel framework for compensating temperature effects on guided waves in composite structures with different thicknesses, based on both theoretical and experimental investigations. The developed method proposes compensation factors that can be scalable to panels of different thicknesses for composite structures made from the same material. In the theoretical analysis, the relationship between the group velocity and amplitude versus the frequencies and thicknesses are determined by the semi-analytical finite element (SAFE) and tuning curve models. The effects of temperature on group velocity and amplitude are then examined through the acquisition of the temperature-dependent material properties obtained by mechanical tests. The theoretical analysis reveals a consistent trend regarding changes in group velocity and amplitude with temperature for the A0 mode within a specific frequency and thickness range. In the experimental investigation, a temperature database was established by collecting temperature signals from composite structures with a circular sensor layout. These structures possessed thicknesses of 2 mm, 4 mm, and 6 mm, with stacking sequence [0/45/−45/90]ns, n=2,4,6 leading to a quasi-isotropic material behaviour. The scalability of the compensation factors is confirmed by computing the factors for one panel and then applying them to the rest, resulting in effective compensation for temperature differences up to ± 30 °C. Furthermore, the performance of the scalable compensation factors is validated by successfully locating damage even when the current temperature is 30 °C lower than the baseline temperature. By scaling the temperature compensation factors at the panel thickness level and using dimensionless compensation factors derived from one thickness to another, the effect of temperature differences in composite panels with varying thicknesses are effectively compensated. This approach requires less baseline temperature data and enhances the damage localization performance, making it applicable to industrial applications where panels of different thicknesses are commonly utilized.

Real-time temperature distribution reconstruction via linear parameter-varying state-space model and Kalman filter in rack-based cooling data centers

Rapidly growing data centers (DCs) are facing challenges with energy consumption increase and thermal environmental security. Thus, this paper proposed a novel real-time temperature distribution prediction and reconstruction model for rack-based cooling DCs, in order to facilitate efficient energy consumption and thermal environment management. The parameter identification method coupling with inherent physical relationships was employed to obtain the linear parameter-varying state-space model, rapidly and approximately predicting the nonlinear thermal dynamic processes within rack-based cooling DC. Then, Kalman filter was adopted to reconstruct the temperature distribution in real-time based upon the developed linear parameter-varying state-space temperature prediction model and measurement information. This study comprehensively analyzed the temperature estimation performance of the proposed model under various model input biases, different sensor numbers and layouts. The results show that the input biases have significant impacts on the accuracy of the temperature prediction model, e.g., ±5% bias in IT workload resulting in an increase of 0.6 times in the mean absolute error (MAE) of the temperature prediction. The proposed temperature reconstruction model reveals excellent performance: compared to the original temperature prediction model, the MAE was decreased by about 5%, 10%, and 13% for reconstruction scenarios using one, two, and three sensors, respectively.

Wheel Defect Detection Using a Hybrid Deep Learning Approach.

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques' cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

The correlation of ground reaction forces between different simplified pressure sensor layouts and the full layout in flatfoot

The correlation of ground reaction forces between different simplified pressure sensor layouts and the full layout in flatfoot