Impact damage monitoring of FRP pressure vessels based on impact force identification

Atobe, Satoshi; Sugimoto, Sunao; Hu, Ning; Fukunaga, H.

doi:10.1080/09243046.2014.915112

Cited by 9 publications

(12 citation statements)

References 17 publications

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“…Basically, the global shape of an impact force is expected to be similar to a half-sine signal. As pointed out in [30], even in the presence of damage, the global shape of an impact force will still be analogous to a half-sign signal with local fluctuations. Scale parameter controls the width of the half-sine.…”

Section: ( )mentioning

confidence: 93%

Automated algorithm for impact force identification using cosine similarity searching

Kalhori

Alamdari

2018

Measurement

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A similarity searching technique is adopted to identify the impact force applied on a rectangular carbon fibre-epoxy honeycomb composite panel. The purpose of this study is to simultaneously identify both the location and magnitude of an unknown impact using the measured dynamic response collected by only a single piezoelectric sensor. The algorithm assumes that a set of impact forces are concurrently applied on a set of pre-defined locations. However, the magnitude of all the impact forces except one is considered to be zero. The impact force at all potential locations is then reconstructed through an l2-norm-based regularisation via two strategies: even-determined approach and under-determined approach. In an evendetermined approach, the reconstruction process is performed independently for each pair of sensor and potential impact location. However, in an under-determined approach, the captured vibration signal is the superposition of the responses of the simultaneous 'assumed' impacts at the potential locations. Using either approach, a reconstructed impact force is obtained for each potential impact location. The reconstructed impact forces at spurious locations are expected to have zero magnitude as no impact has actually occurred at these locations. However, there might

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Section: ( )mentioning

confidence: 93%

Automated algorithm for impact force identification using cosine similarity searching

Kalhori

Alamdari

2018

Measurement

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“…The set of strain gauges is fixed along the beam axis on its bottom face. For [45] where inspections of metallic-based structures are proposed and in [46], a sensor system is devised to monitor impact damage of pressure vessels). The advantage of using an ANN for this application is realized for reasons mentioned above.…”

Section: Sensor Array Examplesmentioning

confidence: 99%

Synthetic Sensor Array Training Sets for Neural Networks

2019

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It is often hard to relate the sensor’s electrical output to the physical scenario when a multidimensional measurement is of interest. An artificial neural network may be a solution. Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources. The same issue arises when the physical measurement is expected to extend across a wide range of values. This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network. Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest. We discuss to which systems our method may be applied. We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array. We present the experimental process, its results, and the resulting accuracies.

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“…In general, identifying an impact load consists of two main aspects: (1) identifying the impact location and (2) reconstructing the impact force time history. A number of studies have been conducted and reported in literature on impact load identification for composite structures during the past two decades . To identify the impact location alone, a straightforward approach is triangulation using the time information of impact‐induced stress waves .…”

Section: Introductionmentioning

confidence: 99%

“…To reconstruct the impact force time history, besides the machine learning methods such as the neural network , most of the methods reported in literature can be generally categorized as model‐based methods. The models characterize the dynamic properties of composite structures under impact, which can be obtained through experiments , analytical solutions or numerical methods . By comparing the measured outputs with the estimated responses from a model, impact force time history can be reconstructed by different kinds of algorithms.…”

Section: Introductionmentioning

confidence: 99%

Impact load identification for composite structures using Bayesian regularization and unscented Kalman filter

Yan

Sun

Büyüköztürk

2016

Struct. Control Health Monit.

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In structural health monitoring of composite structures, one important task is to detect and identify the low-velocity impact events, which may cause invisible internal damages. This paper presents a novel approach for simultaneously identifying the impact location and reconstructing the impact force time history acting on a composite structure using dynamic measurements recorded by a sensor network. The proposed approach consists of two parts: (1) an inner loop to reconstruct the impact force time history and (2) an outer loop to search for the impact location. In the inner loop, a newly developed inverse analysis method with Bayesian inference regularization is employed to solve the ill-posed impact force reconstruction problem using a state-space model. In the outer loop, a nonlinear unscented Kalman filter (UKF) method is used to recursively estimate the impact location by minimizing the error between the measurements and the predicted responses. The newly proposed impact load identification approach is illustrated by numerical examples performed on a composite plate. Results have demonstrated the effectiveness and applicability of the proposed approach to impact load identification.

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Impact damage monitoring of FRP pressure vessels based on impact force identification

Cited by 9 publications

References 17 publications

Automated algorithm for impact force identification using cosine similarity searching

Automated algorithm for impact force identification using cosine similarity searching

Synthetic Sensor Array Training Sets for Neural Networks

Impact load identification for composite structures using Bayesian regularization and unscented Kalman filter

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