Optimal Sensor Configuration Research Articles

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

The wind turbine gearbox, an important component of wind turbines, has a high failure rate and maintenance cost; therefore, several vibration sensors are installed to execute effective condition monitoring, fault diagnosis, and prediction, for reducing the downtime caused by faults. The sensor positions should directly and accurately reflect the gearbox operating status and provide several signals to the condition monitoring system. This article studies a gearbox model and the gear tooth faults, and builds a gearbox gear vibration model involving these faults. Using structure analysis, sensor configuration solutions rendering all the tooth faults detectable and all the faults isolable are, respectively, obtained, based on the gear vibration model. The sensor configuration solutions of the wind turbine gearbox can guide wind turbine gearbox sensor installation in practice and support common wind turbine gearbox sensor configuration solutions in theory.

Blind source separation-based optimum sensor placement strategy for structures

Optimal sensor placement (OSP) plays a key role towards cost-effective structural health monitoring (SHM) of flexible structures like tall buildings. In the context of SHM, vibration measurements collected from a large array of sensors involve significant data storage, signal processing, and labor-intensive deployment of sensors. Moreover, with the increasing cost of vibration sensors, it is not possible to install sensors at all locations of the structure. To circumvent these practical challenges, the OSP provides a powerful mathematical framework to estimate unknown structural information based on optimally instrumented sensors located at fewer locations. The proposed research is focused on determination of optimum sensor positions in the multi-degrees-of-freedom system using blind source separation (BSS) method. The underdetermined signal separation capability of the BSS is explored to conduct the modal identification using fewer sensors and the resulting modal parameters are utilized to set up the optimization criterion for optimal sensor configurations. The methodology is illustrated using simulation models with different mass and stiffness distributions under a wide range of ground motion characteristics. Finally, the vibration measurements of the Canton tower data in China are utilized to demonstrate the performance of the proposed OSP technique.

Optimal sensor configuration for positioning seafloor geodetic node

This paper deals with the optimal acoustic sensor configuration for positioning seafloor geodetic node. We use the determinant of Fisher Information Matrix (FIM) to measure the performance of different sensor configuration. This criteria is calculated in both 2D and 3D scenarios on a more practical assumption that range observations have different weights depending on their value, instead of identical weights. Then the uncertainty of initial node position is also taken into consideration for the calculation of determinant of FIM which was often neglected in the optimizing process. We present a kind of optimal configurations based on the maximum of determinant (FIM), which is circular and has different optimal radius at each corresponding scenario. Simulative experiments are carried out to demonstrate the optimal configuration. Practical experiments in South China Sea can further prove the relationship between positioning accuracy, configuration and determinant of FIM.

Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis

Experiments show that operation efficiency and reliability of buildings can greatly benefit from rich and relevant datasets. More specifically, data can be analyzed to detect and diagnose system and component failures that undermine energy efficiency. Among the huge quantity of information, some features are more correlated with the failures than others. However, there has been little research to date focusing on determining the types of data that can optimally support fault detection and diagnosis (FDD). This paper presents a novel optimal feature selection method, named information greedy feature filter (IGFF), to select essential features that benefit building FDD. On one hand, the selection results can serve as reference for configuring sensors in the data collection stage, especially when the measurement resource is limited. On the other hand, with the most informative features selected by the IGFF, the performance of building FDD could be improved and theoretically justified. A case study on air-handling unit (AHU) is conducted based on the dataset of the ASHRAE Research Project 1312. Numerical results show that, compared with several baselines, the FDD performances of conventional classification methods are greatly enhanced by the IGFF.

Optimal placement of sensors and piezoelectric friction dampers in the pipeline networks based on nonlinear dynamic analysis

Experiences of past earthquakes demonstrate that pipeline systems have no proper performance when exposed to severe earthquakes. In this study, sensor and damper placement approaches are presented for doing reliable health monitoring and seismic retrofitting of the piping networks. Since most of the available sensor placement methods are based on modal analysis results, the authors propose a new scheme that relies on the nonlinearity which utilizes nonlinear time history analysis results, and genetic algorithm is selected to act as the methodology of optimization as well. The results demonstrate that the proposed optimal sensor configuration strategy is more accurate and efficient than the extended modal assurance criterion method. To assess the number of sensors, a sensitivity analysis is undertaken in which the number of sensors computed optimally by the proposed algorithm contains the least convergence error. In addition, the number of iterations and the time consumed in the proposed approach are considerably less than the extended modal assurance criterion method. Moreover, the efficiency of the proposed sensor placement scheme was compared with a new algorithm proposed by Sun and Büyüköztürk, named discrete artificial bee colony, where the simulation result demonstrates high accuracy of the proposed sensor configuration approach. The initial time history analysis results show the vulnerable points of the system, which destroyed due to the applied seismic waves. Hence, to enhance the seismic performance of the system, piezoelectric friction dampers are optimally placed, where it can be clearly seen that the optimal arrangement of piezoelectric friction dampers in the piping system can significantly decrease the seismic response.

Reference-free spectroscopic determination of fat and protein in milk in the visible and near infrared region below 1000 nm using spatially resolved diffuse reflectance fiber probe

New technique of diffuse reflectance spectroscopic analysis of milk fat and total protein content in the visible (Vis) and adjacent near infrared (NIR) region (400–995nm) has been developed and tested. Sample analysis was performed through a probe having eight 200-µm fiber channels forming a linear array. One of the end fibers was used for the illumination and other seven – for the spectroscopic detection of diffusely reflected light. One of the detection channels was used as a reference to normalize the spectra and to convert them into absorbance-equivalent units. The method has been tested experimentally using a designed sample set prepared from industrial raw milk standards with widely varying fat and protein content. To increase the modelling robustness all milk samples were measured in three different homogenization degrees. Comprehensive data analysis has shown the advantage of combining both spectral and spatial resolution in the same measurement and revealed the most relevant channels and wavelength regions. The modelling accuracy was further improved using joint variable selection and preprocessing optimization method based on the genetic algorithm. The root mean-square errors of different validation methods were below 0.10% for fat and below 0.08% for total protein content. Based on the present experimental data, it was computationally shown that the full-spectrum analysis in this method can be replaced by a sensor measurement at several specific wavelengths, for instance, using light-emitting diodes (LEDs) for illumination. Two optimal sensor configurations have been suggested: with nine LEDs for the analysis of fat and seven – for protein content. Both simulated sensors exhibit nearly the same component determination accuracy as corresponding full-spectrum analysis.

Optimisation of numbers, types, and locations of wireless sensors with communication, finite supply, and multiple-sensor coverage constraints

The sensor placement problem considered in this paper is to determine optimal sensor configurations satisfying coverage preferences at a minimal cost. This problem is formulated as the binary linear programming problem. The generic formulation is generalised to include several realistic demands, such as a finite sensor supply, single hop wireless sensor communication, and multiple-sensor overlapping coverage. Communication and detection are considered in a probabilistic framework. An exact solution to this problem is computationally demanding (NP-complete) and practically impossible for realistic scenarios. Therefore, a fast algorithm for approximate solutions is used. To evaluate the quality of the approximate solutions, they are compared with exact solutions for cases when the exact solutions are obtainable. The results demonstrate that the approximate solutions are highly satisfactory regardless of the complexity of problem constraints, while an exact algorithm can be impractically slow.

Optimal placement of triaxial sensors for modal identification using hierarchic wolf algorithm

Optimal triaxial sensor placement plays a crucial role in tridimensional modal identification; however, few studies have been conducted on this topic. In this paper, a holistic approach, including a tridimensional optimal criterion and solution method, is proposed for finding the optimal locations to deploy triaxial sensors. The tridimensional optimal criterion is established by combining the tridimensional modal assurance criterion and the redundancy function. The tridimensional modal assurance criterion is deduced from the one-dimensional modal assurance criterion by using the widely accepted Fisher information matrix, adopted to ensure the linear independence of identified tridimensional mode shapes. The redundancy function, which is defined by the similarity index among nodes, is developed to measure the information redundancy and to maintain effective visualization of the identified tridimensional mode shapes. To efficiently find the optimal triaxial sensor configuration with the proposed tridimensional optimal criterion, the hierarchic wolf algorithm (HWA) is developed by imitating the swarm intelligence embedded in the wolf pack. Five strategies, which are termed as coding and spreading wolves, searching behaviors, attacking behaviors, hierarchic population, and distributing food process, are employed to enhance the global searching ability of the HWA. The proposed approach is verified by a benchmark bridge model. The results indicate that the established tridimensional optimal criterion has the capability of ensuring optimal triaxial sensor configurations that make the identified tridimensional mode shapes have features of excellent linear independence and good visualization, and the HWA has strong ability and high efficiency in determining the global optimal triaxial sensor configuration.

Optimal sensor placement methodology for uncertainty reduction in the assessment of structural condition

This paper introduces a novel approach for selecting sensor positions for uncertainty reduction in the assessment of structural condition, where the main difficulty is how to quantify the uncertainty. In order to tackle this problem, a condition index, which is a linear combination of the finite-element model parameters, is defined. By taking the multiplying coefficient equal to the vulnerability index corresponding to each model parameter, the linearized condition index is able to reflect the influence of local damage on the global damage condition. The uncertainty in the estimate of this linearized condition index can be readily quantified from the uncertainty in the updated model parameters. Bayesian finite-element model updating is applied for uncertainty quantification in the model parameters. The procedure of the proposed method is illustrated by designing the optimal sensor configuration for a truss structure model. The simulated damage and condition assessment of the truss structure shows that the proposed method is effective in reducing the uncertainty in the condition assessment. Furthermore, it is demonstrated that the proposed method is useful for a more important reason: it can reduce the uncertainty in the damage assessment of vulnerable substructure. Copyright © 2016 John Wiley & Sons, Ltd.

Optimal Sensor Placement for Acoustic Underwater Target Positioning With Range-Only Measurements

This paper addresses the problem of optimal acoustic sensor placement for underwater target localization in 3-D using range measurements only. By adopting an estimation theoretical framework, the optimal geometric sensor formation that will yield the best achievable performance in terms of target positioning accuracy is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM). For mathematical tractability, it is assumed that the measurements of the ranges between the target and a set of acoustic sensors are corrupted with white Gaussian noise. For the sake of completeness, an explicit analytical result for a generic $n$ -sensor network is first obtained for the case when there is no uncertainty in the prior knowledge about the target position. The result is then extended to the practical case where the target is known to lie inside a region of uncertainty. The optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. In addition, the “spreading” of the configuration is shown to depend on the number of range measurements, target depth, and the probability distribution function that characterizes the prior knowledge about the target position. Results are also obtained for the problem of optimal sensor placement with constraints, namely, by considering that the sensors can be either located at the sea surface or distributed between the surface and the seabed. The connection between 2-D and 3-D scenarios is clarified. Simulation examples illustrate the key results derived.

On optimal sensing and actuation design for an industrial scale steam methane reformer furnace

The spatial temperature distribution in the highly energy‐intensive furnace unit in a steam reforming‐based hydrogen manufacturing plant determines the energy efficiency of the plant. While the fuel distribution among the burners can be manipulated to control the furnace temperature distribution, adequate temperature measurements is a prerequisite. Typical furnaces have hundreds of tubes and burners, and economic considerations dictate that the number of temperature sensors and flow actuators required for automatic temperature optimization be minimized. In this article, we investigate several formulations for the design of the optimal sensor and actuation configurations for an industrial furnace. We initially formulate the optimal sensor placement problem as a bi‐level optimization problem, and exploit the problem structure to obtain an equivalent mixed‐integer linear program formulation. We then provide an extension to the combined sensor and actuator placement. We demonstrate the efficacy of our approach through simulation case studies based on industrial data. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3225–3237, 2016

Optimal Design of Low-Noise Induction Magnetometer in 1 mHz–10 kHz Utilizing Paralleled Dual-JFET Differential Pre-Amplifier

The optimal design of the induction magnetometer in the frequency band from 1 mHz to 10 kHz was analytically and experimentally investigated to achieve a suitable sensor configuration under a given noise equivalent magnetic induction (NEMI) and volume limitation. The transducer model is first analyzed, and the parameters $ {R}$ , $ {L}$ , $ {C}$ , and $\mu _{\rm {app}}$ are calculated. The equivalent input voltage noise that utilizes an n-paralleled dual- Junction Field-Effect Transistor (JFET) differential pre-amplifier, was decreased by $\surd \text{n}$ times, and the equivalent input current noise was increased by $\surd \text{n}$ times. The noise calculation formulas are derived based on the mathematical principle of the flux-feedback induction magnetometer, and the NEMI was immediately calculated. A set of sensor configurations, which reached the given NEMI, were achieved by solving the NEMI equation. The optimal sensor configuration was obtained, considering the smallest coil outer diameter limitation. To verify the theoretical analysis, the optimal induction magnetometer was manufactured, and its NEMI was measured in an electromagnetic shielding room. The experimental results show good agreement with the theoretical analysis. Furthermore, the optimal induction magnetometer had a smaller volume with a similar NEMI level compared with the well-known sensor MFS-06.

Characterization and optimization of an ultrasonic piezo-optical ring sensor

A resonant piezo-optical ring sensor with both piezoelectric and fiber Bragg grating (FBG) sensing elements was assessed for ultrasonic wave detection. The ring sensor is an existing device that has been shown experimentally to exhibit a number of sensing features: omnidirectionality, mode selectivity, and frequency tunability. The present study uses finite element modeling to understand these features as a means to characterize and optimize the sensor. A combined vibration-wave propagation modeling approach was used, where the vibrational modeling provided a basis for understanding sensing features, and the wave propagation modeling provided predictive power for sensor performance. The sensor features corresponded to the fundamental vibrational mode of the sensor, particularly to the base motion of this mode. The vibrational modeling was also used to guide sensor optimization, with an emphasis on the FBG and piezoelectric sensing elements. It was found that sensor symmetry and nodes of extraneous resonance modes could be exploited to provide a single-resonance response. A series of pitch-catch guided wave experiments were performed on a thin aluminum plate to assess the optimized sensor configuration. Tuning curves showed a single-frequency response to a Lamb wave and mechanical filtering away from the dominant frequency; the sensor capability for mechanical amplification of a Lamb wave and mechanical amplification of a pencil-lead-break acoustic emission event were also demonstrated.

Design Optimization of a Magnetic Field-Based Localization Device for Enhanced Ventriculostomy

The accuracy of many freehand medical procedures can be improved with assistance from real-time localization. Magnetic localization systems based on harnessing passive permanent magnets (PMs) are of great interest to track objects inside the body because they do not require a powered source and provide noncontact sensing without the need for line-of-sight. While the effect of the number of sensors on the localization accuracy in such systems has been reported, the spatial design of the sensing assembly is an open problem. This paper presents a systematic approach to determine an optimal spatial sensor configuration for localizing a PM during a medical procedure. Two alternative approaches were explored and compared through numerical simulations and experimental investigation: one based on traditional grid configuration and the other derived using genetic algorithms (GAs). Our results strongly suggest that optimizing the spatial arrangement has a larger influence on localization performance than increasing the number of sensors in the assembly. We found that among all the optimization schemes, the approach utilizing GA produced sensor designs with the smallest localization errors.

Optimized Design and Calibration of the Triaxis Induction Magnetometer with Crosstalk and Nonorthogonality Compensation

An optimized triaxis induction magnetometer has been designed and calibrated to minimize the influences from the nonorthogonality and the magnetic flux crosstalk. Utilizing the nonlinear least square method, contributions due to the nonorthogonal assembly of three transducers are cancelled. The magnetic flux crosstalk is a frequency-dependent error component in the calibration of the triaxis induction magnetometer. Influences from the assembly density, the frequency, and the feedback amount are analyzed theoretically, and an optimized sensor configuration which has a smaller crosstalk is achieved. Moreover, a mathematical compensation algorithm has also been utilized to suppress the residues crosstalk ulteriorly. To validate the theoretical analysis, a triaxis induction magnetometer was manufactured and the experiment setup has also been built. The experiment results show that the cross-outputs of the transverse induction magnetometers have been significantly decreased about two orders, indicating that the proposed method is applicable for the triaxis induction magnetometer.

TDOA Measurement Based GDOP Analysis for Radio Source Localization

The revolution brought by GPS has lead to the development of various positioning applications. These applications use measurements (travel time of signal or time of flight) in determining the position. The time of flight requirement in GPS has restricted its use in positioning of unknown objects. Whereas, localization of an unknown enemy Radio Source (URS) such as enemy radar system, tracking of Unmanned Aerial Vehicle (UAV) etc., have high demand in the field of defence in a country like India, they require a new type of measurement technique called Time difference of Arrival (TDOA). There are various factors that affect the position accuracy including amount of measurement noise, algorithm employed for positioning and sensor URS geometry. The sensor-URS geometry is one of the most predominant factors in determining the accuracy estimate and is referred to as Geometry Dilution of Precision (GDOP). This is a well defined problem in positioning systems that use GPS/Time of arrival (TOA) measurements. However, it needs to be refined for URS localization systems/TDOA measurements. This paper mainly focuses on explaining and deriving the concepts of GDOP in relation to TDOA measurement based URS localization systems. For a comprehensive understanding, an illustrative example of localizing an URS with TDOA measurements is explained and discusses the effect of sensor geometry with the help of GDOP profiles. In addition, this paper explains the process of identifying an optimal sensor configuration for URS localization systems. For the purpose of simulation, five sensors arranged in two different configurations are considered. A target surveillance area of 3600 Sq-Kms with 169 target zones is used in generation of GDOP profiles over the Indian subcontinent.

Improving tunnel resilience against fires: A new methodology based on temperature monitoring

Monitoring temperatures during tunnel fires is of major importance for both the firefighters extinguishing the fire, and the engineers in charge of the subsequent repair work. However, current methods of assessing fire damage have limitations when applied to tunnels and only provide estimates of the maximum fire temperatures at specific locations of the tunnel. This is not a desirable situation, as the temperature–time curves associated with the fire event should be available for use in assessing the residual strength of the tunnel structure. This is the key parameter in defining repair work and the length of time the tunnel will need to be closed and thus the socio-economic cost of the tunnel fire. In addition, real-time recording of the temperature–time curves would provide valuable information to the firefighters engaged in extinguishing the fire.This paper presents a new general methodology for the optimal placement of sensors in a tunnel to obtain the temperature evolution at any point along its lining during a fire. The methodology was applied to the Virgolo Tunnel in Italy, in which 100 potential high-temperature sensor configurations were tested and a set of optimal sensor configurations was proposed. The results of the analysis show that: (a) the proper location of the sensors is crucial; (b) it is possible to define a set of sensor configurations that minimize the cost of the monitoring system and maximize the accuracy of the estimated temperatures; (c) it is important to place at least three high-temperature sensors in each monitored cross section (at the crown and symmetrically on the haunches/side walls). The proposed methodology improves tunnel resilience against fires, as it enables safer infrastructure and a faster and more economic recovery of the tunnel after a fire event.

Optimal redundant sensor configuration for accuracy increasing in space inertial navigation system

A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyzes the performance of redundant IMUs and their various sensors configurations. The inertial instruments can achieve high reliability for long periods of time only by redundancy. By suitable geometric configurations it is possible to extract the maximum amount of reliability and accuracy from a given number of redundant single-degree-of-freedom gyros or accelerometers. This paper gives general derivation of the optimum matrix which can be applied to the outputs of any combination of 3 or more sensors to obtain 3 orthogonal vector components based on their geometric configuration and error characteristics. Certain combinations of 4 or more instruments have the capability of detecting an instrument malfunction, those of 5 additional capabilities of isolating that malfunction to a particular sensor.

Gyroscopic sensing in the wings of the hawkmoth Manduca sexta: the role of sensor location and directional sensitivity

The wings of the hawkmoth Manduca sexta are lined with mechanoreceptors called campaniform sensilla that encode wing deformations. During flight, the wings deform in response to a variety of stimuli, including inertial-elastic loads due to the wing flapping motion, aerodynamic loads, and exogenous inertial loads transmitted by disturbances. Because the wings are actuated, flexible structures, the strain-sensitive campaniform sensilla are capable of detecting inertial rotations and accelerations, allowing the wings to serve not only as a primary actuator, but also as a gyroscopic sensor for flight control. We study the gyroscopic sensing of the hawkmoth wings from a control theoretic perspective. Through the development of a low-order model of flexible wing flapping dynamics, and the use of nonlinear observability analysis, we show that the rotational acceleration inherent in wing flapping enables the wings to serve as gyroscopic sensors. We compute a measure of sensor fitness as a function of sensor location and directional sensitivity by using the simulation-based empirical observability Gramian. Our results indicate that gyroscopic information is encoded primarily through shear strain due to wing twisting, where inertial rotations cause detectable changes in pronation and supination timing and magnitude. We solve an observability-based optimal sensor placement problem to find the optimal configuration of strain sensor locations and directional sensitivities for detecting inertial rotations. The optimal sensor configuration shows parallels to the campaniform sensilla found on hawkmoth wings, with clusters of sensors near the wing root and wing tip. The optimal spatial distribution of strain directional sensitivity provides a hypothesis for how heterogeneity of campaniform sensilla may be distributed.

Optimal sensor placement under uncertainties using a nondirective movement glowworm swarm optimization algorithm

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.