The sensor placement methods for the modal identification are usually based on the prediction error between the measured and the structural model responses. Generally, the prediction errors, which are caused by the mo...
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The sensor placement methods for the modal identification are usually based on the prediction error between the measured and the structural model responses. Generally, the prediction errors, which are caused by the model error and the measurement noise, are assumed to be a Gaussian random vector together. This paper proposes a conditional information entropy based sensor placement method to separately investigate the influences of the measurement noise and the model error on the sensorplacement. The changes in the element stiffness matrices are used to represent the model error. To quantify the uncertainty in the modal identification result, the conventional information entropy method is not suitable, due to the uncertainty in the Fisher matrix caused by the uncertain model error. This paper proposes a conditional information entropy index to quantify the uncertainty in the modal identification result with uncertain Fisher matrices. The optimal sensorplacement corresponds to the one that maximizes the conditional information entropy index value over the set of all possible sensorplacements. The conventional information entropy method can be seen as a special case of the proposed conditional information entropy method. In addition, the proposed sensor placement method can be applied to the placement of the multidimensional sensors. The simply supported beam and a bridge benchmark structure are used to explain the sensor placement method, and the redundancy threshold can be set to avoid redundant information provided by closely spaced sensors. (C) 2019 Elsevier Ltd. All rights reserved.
Optimally deploy sparse sensors for better damage identification and structural health monitoring is always a challenging task. The Effective Independence(EI) is one of the most influential sensor placement method and...
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Optimally deploy sparse sensors for better damage identification and structural health monitoring is always a challenging task. The Effective Independence(EI) is one of the most influential sensor placement method and to be discussed in the paper. Specifically, the effect of the different weighting coefficients on the maximization of the Fisher information matrix(FIM) and the physical significance of the re-orthogonalization of modal shapes through QR decomposition in the EI method are addressed. By analyzing the widely used EI method, we found that the absolute identification space put forward along with the EI method is preferable to ensuring the maximization of the FIM, instead of the original EI coefficient which was post-multiolied by a weighting matrix. That is, deleting the row with the minimum EI coefficient can't achieve the objective of maximizing the trace of FIM as initially conceived. Furthermore, we observed that in the computation of EI method, the sum of each retained row in the absolute identification space is a constant in each iteration. This potential property can be revealed distinctively by the product of target mode and its transpose, and its form is similar to an alternative formula of the EI method through orthogonal-triangular(QR) decomposition previously proposed by the authors. With it, the physical significance of re-orthogonalization of modal shapes through QR decomposition in the computation of EI method can be obviously manifested from a new perspective. Finally, two simple examples are provided to demonstrate the above two observations.
Coverage is regarded as one of the important quality of service merits of a wireless sensor network (WSN) to evaluate its monitoring capability. In this study, the authors develop a novel quantitative method to evalua...
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Coverage is regarded as one of the important quality of service merits of a wireless sensor network (WSN) to evaluate its monitoring capability. In this study, the authors develop a novel quantitative method to evaluate the quality of coverage of sensors. Deployment entropy is introduced to measure non-uniformity in sensor coverage, which could reveal the quality of sensor deployment without considering the sensing region of the sensors. Moreover, the authors develop a scheme of activating pre-deployed sensors based on the result of deployment entropy, which is to activate additional pre-deployed sensors in keeping with the tendency of increasing the deployment entropy. The simulation results show that it can improve the sensing coverage with less active sensors than the other sensor placement method.
This paper discusses an extension of the MinMAC algorithm to aid sensorplacement for modal tests. The extension is, essentially, a forward-backward combinational MinMAC algorithm. The original MinMAC algorithm propos...
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ISBN:
(纸本)9781605600666
This paper discusses an extension of the MinMAC algorithm to aid sensorplacement for modal tests. The extension is, essentially, a forward-backward combinational MinMAC algorithm. The original MinMAC algorithm proposed by Carne can be regarded as a forward sequential MinMAC algorithm, which maximizes the discrimination between mode shapes of interest starting from a small intuition set. The proposed forward-backward combinational MinMAC algorithm aims to combine advantages of both forward addition and backward deletion MinMAC approaches. Moreover, the proposed algorithm is applied to the I-40 Bridge and the result is compared with that of the Effective Independence method. Furthermore, discussions of influencing sensor placement methods and their connections based on our resent results are presented, through which the insight of sensorplacement problem is clearly interpreted. In particular, the physical significance and efficient computation of the Effective Independence method are introduced. Finally, comparison of sensor placement methods on a ladder structure and evaluation criteria are discussed.
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