Efficient sensor placement for state estimation in structural dynamics

Hernández, Eduardo

doi:10.1016/j.ymssp.2016.09.005

Cited by 28 publications

(12 citation statements)

References 17 publications

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“…A difference in MSE is justified by the sensor positioning. A performance improvement can be obtained by optimally placing the transducers [51]. Next, the central graph shows n α * i|i for each window, which depends on the choice of ε α .…”

Section: Resultsmentioning

confidence: 99%

Exploiting input sparsity for joint state/input moving horizon estimation

Kirchner

Croes

Cosco

et al. 2018

Mechanical Systems and Signal Processing

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This paper proposes a novel time domain approach for joint state/input estimation of mechanical systems. The novelty consists of exploiting compressive sensing (CS) principles in a moving horizon estimator (MHE), allowing the observation of a large number of input locations given a small set of measurements. Existing techniques are characterized by intrinsic limitations when estimating multiple input locations, due to an observability decrease. Moreover, CS does not require an input to be characterized by a slow dynamics, which is a requirement of other state of the art techniques for input modeling. In the new approach, called compressive sensing-moving horizon estimator (CS-MHE), the capability of the MHE of minimizing the noise while correlating a model with measurements is enriched with an 1 -norm optimization in order to promote a sparse solution for the input estimation. A numerical example shows that the CS-MHE allows for an unknown input estimation in terms of magnitude, time and location, exploiting the assumption that the input is sparse in time and space. Finally, an experimental setup is presented as validation case.

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Section: Resultsmentioning

confidence: 99%

Exploiting input sparsity for joint state/input moving horizon estimation

Kirchner

Croes

Cosco

et al. 2018

Mechanical Systems and Signal Processing

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“…Kalman filter algorithm, 185 which estimates the state vector and its corresponding error variances, is integrated with sequential placement strategies to address OSP problems. 39,186 Besides, many other methodologies, such as backup sensor-based fault-tolerance SHM method, 30 energy-efficient sensor deployment, 76 frequency domain–based OSP technique, 187 mixed variable pattern search algorithm, 188 Gram–Schmidt orthogonalization procedure, 72 three-phase sensor placement approach, 29 e-Estimator algorithm, 75 and wave propagation-based local interaction simulation approach, 189 are also available for OSP. More development details of these methodologies can be referred to the associated studies.…”

Section: Optimization Methodologiesmentioning

confidence: 99%

Computational methodologies for optimal sensor placement in structural health monitoring: A review

Tan

Zhang

2019

Structural Health Monitoring

108

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Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement is critical in the structural health monitoring system as the sensor configuration directly impacts the quality of collected data used for structural health diagnosis. Therefore, this study presents a comprehensive review of computational methodologies for optimal sensor placement in structural health monitoring. The problem formulation of optimal sensor placement is first introduced, including commonly used evaluation criteria for sensor configurations. Then, various existing optimization methodologies for sensor placement are summarized and introduced in detail, especially for the evolutionary algorithms and their improved variants. Finally, the suitability of computational methods for specific structural health monitoring applications is also discussed. The main goal of this study is to deliver a comprehensive reference of computational methodologies for optimal sensor placement in structural health monitoring studies and applications. This article is concluded by highlighting the most widely utilized evaluation criteria and optimization methodologies for sensor configuration determination.

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“…Optimal sensor placement and sensor networks is a wellstudied topic across a range of real-time applications, including structural damage detection [2][3][4][5][6], structural health monitoring [7], combined sensor and actuator location for intelligent flexible structures [8,9], gas turbine engine health monitoring [10], robot localization [11], and wireless or mobile sensor networks [12,13]. Similar sensor placement studies have been conducted for energy efficient buildings [14], data centers [15], outdoor wind studies [16], soil moisture monitoring [17], agriculture [18], wind and water quality [19], water leak detection [20], and chemical processes [21].…”

Section: Related Workmentioning

confidence: 99%

Optimal Sensor Placement Methods for Active Power Electronic Systems

Peddada

Tannous

Alleyne

et al. 2017

Volume 2A: 43rd Design Automation Conference

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Accurate temperature estimation of high density active power electronic systems is vital for dynamic thermal management. Accurate and reliable estimation is especially important in regions that are close to failure, either due to high temperature or significant materials or component sensitivity. Improved estimation can support lower safety factors and enhanced system performance. An investigation of optimal temperature sensor placement methods is presented here, focusing primarily on methods utilizing information-based metrics. In addition, physics-based metrics are explored in an initial study that may have the potential to be more closely aligned with overall system utility. Studies are based on a 2 kW, single-phase, seven-level, GaN-based inverter. A lumped-parameter reduced-order thermal model, developed in previous work, is used for real-time temperature estimation. A continuous relaxation of a 2D placement domain led to a novel linear programming formulation that supports solution of finely-discretized sensor placement problems with minimal computational expense. Improved sensor placement performance metrics account for multiple loading conditions and estimation accuracy with respect to failure prevention.

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Efficient sensor placement for state estimation in structural dynamics

Cited by 28 publications

References 17 publications

Exploiting input sparsity for joint state/input moving horizon estimation

Exploiting input sparsity for joint state/input moving horizon estimation

Computational methodologies for optimal sensor placement in structural health monitoring: A review

Optimal Sensor Placement Methods for Active Power Electronic Systems

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