Compressive sensing-based lost data recovery of fast-moving wireless sensing for structural health monitoring

Bao, Yuequan; Yu, Yan; Li, Hui; Mao, Xingquan; Jiao, Wenfeng; Zou, Zilong; Ou, Jinping

doi:10.1002/stc.1681

Cited by 65 publications

(47 citation statements)

References 31 publications

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“…The distribution restoration method is mainly applied to monitoring data that can be described by random variables. For acceleration data, several signal reconstruction methods (e.g., compressive sampling [19][20][21] or ℓ1-minimization [5]) have been successfully applied to recover missing records from a deterministic perspective; the distribution restoration method presented in this article is not designed for acceleration data.…”

Section: Description Of Problem and Basic Assumptionsmentioning

confidence: 99%

“…where refers to undetermined vector coefficients with the same dimensions as * , and is a reproducing kernel corresponding to the RKHS H( ). For the function-to-vector regression model, the reproducing kernel is an operator-valued kernel that maps from the functional space valued kernel is the Gaussian operator kernel, i.e., (20) where I identity is the identity operator of vectors (i.e., I identity = , ∀ ∈ R 1× ), and is the parameter of the Gaussian operator kernel. A related discussion on the design of different types of operator-valued kernels can be found in [36].…”

Section: Function-to-vector Regression Modelmentioning

confidence: 99%

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LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Chen

Bao

et al. 2019

Mechanical Systems and Signal Processing

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Data loss is a critical problem in structural health monitoring (SHM). Probability distributions play a highly important role in many applications. Improving the quality of distribution estimations made using incomplete samples is highly important. Missing samples can be compensated for by applying conventional missing data restoration methods; however, ensuring that restored samples roughly follow underlying distributions of true missing data remains a challenge. Another strategy involves directly restoring the probability density function (PDF) for a sensor when samples are missing by leveraging distribution information from another sensor with complete data using distribution regression techniques; existing methods include the conventional distribution-to-distribution regression (DDR) and distribution-to-warping function regression (DWR) methods. Due to constraints on PDFs and warping functions, the regression functions of both methods are estimated from the Nadaraya-Watson kernel estimator with relatively low degrees of precision. This article proposes a new indirect distribution-to-distribution regression method in the context of functional data analysis for restoring distributions of missing SHM data. PDFs are transformed to ordinary functions residing in a Hilbert space via the newly proposed log-quantile-density (LQD) transformation; the regression for distributions is realized in the transformed space via a functional regression model constructed based on the theory of Reproducing Kernel Hilbert Space (RKHS),corresponding result is subsequently mapped back to the density space through the inverse LQD transformation. Test results using field monitoring data indicate that the new method significantly outperforms conventional methods in general cases; however, in extrapolation cases, the new method is inferior to the distribution-to-warping function regression method.

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Section: Description Of Problem and Basic Assumptionsmentioning

confidence: 99%

Section: Function-to-vector Regression Modelmentioning

confidence: 99%

LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Chen

Bao

et al. 2019

Mechanical Systems and Signal Processing

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“…The main reason is that although the ℓ 1 norm is weaker than the p < 1 norm in ensuring sparsity (as shown in Figure 1), ℓ 1 -regularized optimization is a convex problem and admits efficient solution via linear programming techniques [27,35]. Then Equation (2) is solved in a sparse regularization strategy as Equation (12).…”

Section: Sparse Regularizationmentioning

confidence: 99%

“…In Ref. [11,12], compressed sensing techniques were used in data communication among wireless sensor nodes, where ℓ 1 -norm regularization was employed to reconstruct time series measurements of acceleration. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Comparative studies on damage identification with Tikhonov regularization and sparse regularization

Zhang

2015

Struct. Control Health Monit.

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SUMMARYStructural damage identification is essentially an inverse problem. Ill-posedness is a common obstacle encountered in solving such an inverse problem, especially in the context of a sensitivity-based model updating for damage identification. Tikhonov regularization, also termed as ℓ 2 -norm regularization, is a common approach to handle the illposedness problem and yields an acceptable and smooth solution. Tikhonov regularization enjoys a more popular application as its explicit solution, computational efficiency, and convenience for implementation. However, as the ℓ 2 -norm term promotes smoothness, the solution is sometimes over smoothed, especially in the case that the sensor number is limited. On the other side, the solution of the inverse problem bears sparse properties because typically, only a small number of components of the structure are damaged in comparison with the whole structure. In this regard, this paper proposes an alternative way, sparse regularization, or specifically ℓ 1 -norm regularization, to handle the ill-posedness problem in response sensitivity-based damage identification. The motivation and implementation of sparse regularization are firstly introduced, and the differences with Tikhonov regularization are highlighted. Reweighting sparse regularization is adopted to enhance the sparsity in the solution. Simulation studies on a planar frame and a simply supported overhanging beam show that the sparse regularization exhibits certain superiority over Tikhonov regularization as less false-positive errors exist in damage identification results. The experimental result of the overhanging beam further demonstrates the effectiveness and superiorities of the sparse regularization in response sensitivity-based damage identification.

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“…Lately, a new scheme based on compressed sensing, which is able to recover the original data from the received incomplete (lost during wireless communication) data, has been proposed for application in wireless structural health monitoring [29][30][31]. Nevertheless, it does not address the compression issue for efficient data transmission.…”

Section: Introductionmentioning

confidence: 99%

Robust data transmission and recovery of images by compressed sensing for structural health diagnosis

Yang

Nagarajaiah

2016

Struct. Control Health Monit.

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SUMMARYDigital cameras are cost-effective vision sensors and able to directly provide two-dimensional information of structural condition in monitoring and assessment applications. For example, digital cameras are essential components of unmanned aerial vehicles (UAVs) and robotic agents for mobile sensing and inspection of pipelines, buildings, transportation infrastructure, etc, especially in post-natural disaster and man-made extreme events assessment. Additionally, while surveillance cameras have been widely used for transportation systems (e.g., traffic monitoring), if appropriately mounted on the large-scale structures such as the bridges, they can continuously monitor the structural condition under operational loads and hazards, complementing the regular visual inspection and assessment conducted by experts. In these or other applications, efficiently and reliably transferring the structural images or videos, which are as such largescale, are important and challenging, especially in wireless platform that is either required (e.g., UAVs and robotic agents) or more suitable (e.g., camera monitoring networks) with only limited power and communication resources. This paper studies the computational algorithms for efficient and reliable transmission of the structural monitoring images; in particular, the compressed sensing (CS) technique is explored for robust data transmission and recovery. The sparse representation or data structure of the structural images is exploited, leading to the CS based central strategy: on some sparse domain, randomly encode large-scale image data into few relevant coefficients, which are then transferred (robust to random data loss) and recovered (in base station) for subsequent structural health diagnosis. Image data of bench scale pipe structure, concrete structure and full scale stay cable are employed for validation of the CS based method. Its performance is also compared with traditional transform coding and low-dimensional encoding ( age data subjected to random data loss for structural health diagnosis. 2. Exploit the sparsity and randomness of compressed sensing for efficient and robust image data transmission. 3. Accurate image recovery without losing structural health diagnostic quality from highly undersampled measurements. 4. Demonstrations and comparisons with other methods by four structural images of laboratory and field settings.

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Compressive sensing-based lost data recovery of fast-moving wireless sensing for structural health monitoring

Cited by 65 publications

References 31 publications

LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Comparative studies on damage identification with Tikhonov regularization and sparse regularization

Robust data transmission and recovery of images by compressed sensing for structural health diagnosis

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