Optimal Sensor Placement through Bayesian Experimental Design: Effect of Measurement Noise and Number of Sensors

Capellari, Giovanni; Chatzi, Eleni; Mariani, Stefano

doi:10.3390/ecsa-3-d006

Cited by 13 publications

(10 citation statements)

References 9 publications

(9 reference statements)

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“…Here, the objective function is computed at the following discrete points of the grid

. As expected, the maximum value of the expected Shannon information gain increases as the number of sensors increases, as analytically proven in [ 6 ] and numerically shown in [ 72 ], while the standard deviations decreases, since more information is provided by the SHM system. The associated optimal sensor configuration, which corresponds to the maximum of the objective functions, is shown in Figure 4 .…”

Section: Results: Application To the Monitoring Of A Tall Buildingsupporting

confidence: 66%

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

Capellari

Chatzi

Mariani

2018

Sensors

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Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost–benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information–cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

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“…Here, the objective function is computed at the following discrete points of the grid

Section: Results: Application To the Monitoring Of A Tall Buildingsupporting

confidence: 66%

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

Capellari

Chatzi

Mariani

2018

Sensors

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“…Similar approaches by using search metaheuristics were also delivered by Yi et al [18], Zhao et al [19], and Shan et al [20]. In more recent study, Capellari et al [21] proposed an optimal sensor placement by employing Polynomial Chaos Expansion and stochastic optimization to maximize the gain in Shannon information.…”

Section: Introductionmentioning

confidence: 89%

Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring

2018

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Abstract:In this paper, we investigated transducer placement strategies for detecting cracks in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage-tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

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“…In what follows, a Kriging surrogate modelbased design procedure is presented to identify uncertain variables that provoke high variability in the system performance in order to improve the robustness of an HPCS. Note that other established techniques to perform sensitivity analysis of engineered systems exist in literature (Saltelli 2002), including Bayesian (Capellari et al 2016), polynomial chaos-expansion (Sudret 2008;Blatman and Sudret 2010), and Monte Carlo (Zio and Pedroni 2012; Ahmed et al 2019) approaches. In this study, the surrogate model-based procedure presented in (Micheli et al 2020b) has been selected to build on findings that Kriging was a computationally fast, accurate and promising tool enabling the robust design of HPCS under uncertainty.…”

Section: Robust Design Of Hpcs Under Uncertaintiesmentioning

confidence: 99%

Kriging-Based Design for Robust High-Performance Control Systems

Micheli

Laflamme

2020

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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High-performance control systems (HPCS) are sophisticated vibration-mitigation devices that include active, semiactive, and hybrid systems. HPCS leverage feedback mechanisms to dynamically adjust the damping force in response to motion, offering a good mitigation performance over a wide excitation bandwidth. These damping systems are attractive for multihazard mitigation of civil structures. However, the application of HPCS necessitates the design and integration of a closed-loop control system that can be susceptible to local failure or reduction in performance in the form of, for example, malfunctions of HPCS components, noisy measurements, wear, aging, and power unavailability. It follows that these sources of uncertainties may cause concerns and impede the wide acceptability and deployment of HPCS. A solution is to enhance the robustness of the closed-loop configuration through, for example, added redundancies and/or more robust, yet more expensive, components. In this paper, a Kriging-based design procedure is presented enabling the robust design of closed-loop control systems. The design procedure consists of constructing a Kriging surrogate that maps uncertainties to structural performance, and using that surrogate to identify the most influential sources of uncertainties. After, these identified uncertainties are made more robust in order to decrease the variance in the HPCS performance over the lifetime of the structure. The performance of the HPCS is directly quantified through a life cycle cost analysis to obtain probability distributions on the expected financial impacts. The proposed procedure is demonstrated on a numerically simulated 39-story structure exposed to wind loads. Uncertainties in both the HPCS configuration (i.e., damping devices and sensors) and external load are considered. Results demonstrate that the proposed framework can be used to substantially improve the robustness of the closed-loop system. Also, through a comparison against a robust passive-viscous case, it is shown that robustly designed HPCS significantly outperform a passive system in terms of vibration mitigation and life cycle costs.

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Optimal Sensor Placement through Bayesian Experimental Design: Effect of Measurement Noise and Number of Sensors

Cited by 13 publications

References 9 publications

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

Cost–Benefit Optimization of Structural Health Monitoring Sensor Networks

Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring

Kriging-Based Design for Robust High-Performance Control Systems

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