＜正＞For works related to the sensor placement problem in the literature,the generally used discrete-coordinate systems modeled by the finite element（FE） can only approximate their actual dynamic behavior,and the s...
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ISBN:
(纸本)9787030357878
＜正＞For works related to the sensor placement problem in the literature,the generally used discrete-coordinate systems modeled by the finite element（FE） can only approximate their actual dynamic behavior,and the sensors are confined to be put only on discrete nodes corresponding to the coarse mesh scheme usually *** optimalsensor placement methodology for the continuous-coordinate structures with typical distributed-parameter properties has been proposed by the authors to minimize the uncertainties associated with the identified structural modeling parameters, where the information entropy is employed as a measure to quantify the *** optimal sensor configuration is obtained by minimizing the information entropy through a continuous optimization ***,in the obtained optimalconfiguration results for the continuous-coordinate structures,some sensors tend to be infinitely close to each other,and the main purpose of this paper to introduce an effective approach to overcome this drawback.
While the objective of structural design is to achieve stability with an appropriate level of reliability, the design of systems for structural health monitoring is performed to identify a configuration that enables a...
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ISBN:
(纸本)9781628415384
While the objective of structural design is to achieve stability with an appropriate level of reliability, the design of systems for structural health monitoring is performed to identify a configuration that enables acquisition of data with an appropriate level of accuracy in order to understand the performance of a structure or its condition state. However, a rational standardized approach for monitoring system design is not fully available. Hence, when engineers design a monitoring system, their approach is often heuristic with performance evaluation based on experience, rather than on quantitative analysis. In this contribution, we propose a probabilistic model for the estimation of monitoring system effectiveness based on information available in prior condition, i.e. before acquiring empirical data. The presented model is developed considering the analogy between structural design and monitoring system design. We assume that the effectiveness can be evaluated based on the prediction of the posterior variance or covariance matrix of the state parameters, which we assume to be defined in a continuous space. Since the empirical measurements are not available in prior condition, the estimation of the posterior variance or covariance matrix is performed considering the measurements as a stochastic variable. Moreover, the model takes into account the effects of nuisance parameters, which are stochastic parameters that affect the observations but cannot be estimated using monitoring data. Finally, we present an application of the proposed model to a real structure. The results show how the model enables engineers to predict whether a sensorconfiguration satisfies the required performance.
The optimalsensor placement problem consists of determining the number, types, and locations of sensors satisfying inhomogeneous coverage requirements while minimising a specified cost function. The cost function can...
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The optimalsensor placement problem consists of determining the number, types, and locations of sensors satisfying inhomogeneous coverage requirements while minimising a specified cost function. The cost function can reflect various factors such as the actual cost of the sensors, their total number, and energy consumption. A strict and general formulation of the problem is described here for sensors characterised by probability of detection at some specified probability of false alarm. The formulation includes non-uniform coverage preferences and realistic, non-line-of-sight detection accounting on signal propagation effects. The optimisation is expressed as a solution to a binary linear programming problem. While exact solution of this problem is typically prohibitive, a fast greedy algorithm is presented that yields a near-optimal solution. It can also be successfully applied to improve coverage of an existing sensor network. This approach compares very favourably to an alternative heuristic strategy based on placing sensors one-by-one in the previously worst-covered location.
This paper considers the optimal sensor configuration for inertial navigation systems which have redundant inertial sensors such as gyroscopes and accelerometers. We suggest a method to determine the optimalsensor co...
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This paper considers the optimal sensor configuration for inertial navigation systems which have redundant inertial sensors such as gyroscopes and accelerometers. We suggest a method to determine the optimal sensor configuration which considers both the navigation and FDI performance. Monte Carlo simulations are performed to show the performance of the suggested optimal sensor configuration method.
Extract modal displacements from sensor outputs, known as modal sensing, is a key issue in independent modal space control. Spillover in the sensed modal displacements is usually inevitable when finite number of discr...
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ISBN:
(纸本)0780393953
Extract modal displacements from sensor outputs, known as modal sensing, is a key issue in independent modal space control. Spillover in the sensed modal displacements is usually inevitable when finite number of discrete sensors are used. This paper investigate the influence of the sensorconfiguration on spillover level and robustness of the modal sensor. It's found that the condition number of the modal sensor is the key factor. Thus, an optimization problem is developed to determine the optimal sensor configuration. The modified simulated annealing algorithm is employed to solve the problem. An clamped beam with five piezoelectric sensors is modeled using the finite method. Numerical simulations verified the theoretical analysis.
A statistical system identification methodology is applied for performing parametric identification and fault detection studies in nonlinear vehicle systems. The vehicle nonlinearities arise due to the function of the...
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A statistical system identification methodology is applied for performing parametric identification and fault detection studies in nonlinear vehicle systems. The vehicle nonlinearities arise due to the function of the suspension dampers, which assume a different damping coefficient in tension than in compression. The suspension springs may also possess piecewise linear characteristics. These lead to models with parameter discontinuities. Emphasis is put on investigating issues of unidentifiability arising in the system identification of nonlinear systems and the importance of sensorconfiguration and excitation characteristics in the reliable estimation of the model parameters. A methodology is proposed for designing the optimal sensor configuration (number and location of sensors) so that the corresponding measured data are most informative about the condition of the vehicle. The effects of excitation characteristics on the quality of the measured data are systematically explored. The effectiveness of the system identification and the optimal sensor configuration design methodologies is confirmed using simulated test data from a classical two-degree-of-freedom quarter-car model as well as from more involved and complete vehicle models, including four-wheel vehicles with flexible body.
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