Efficient structural health monitoring for a benchmark structure using adaptive RLS filters

Chase, J. Geoffrey; Begoc, Vincent; Barroso, Luciana R.

doi:10.1016/j.compstruc.2004.11.005

Cited by 32 publications

(24 citation statements)

References 16 publications

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“…In the adaptive RLS filtering, the coefficients are adjusted from sample-to-sample to minimise the mean square error (MSE) between the measured noisy scalar signal and its modelled value from the filter (Chase et al 2005). The scalar, real output signal, y k , is measured at a discrete time k, in response to a set of scalar input signals, X k (i), i = 1, 2, … n, where n is an arbitrary number of filter taps.…”

Section: Adaptive Rls Filteringmentioning

confidence: 99%

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Hao¹,

Zhao²,

Chen³

et al. 2009

Czech J. Food Sci.

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Lin H., Zhao J., Chen Q., Cai J., Zhou P. (2009): Eggshell crack detection based on acoustic impulse response and supervised pattern recognition. Czech J. Food Sci., 27: 393-402.A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

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Section: Adaptive Rls Filteringmentioning

confidence: 99%

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Hao¹,

Zhao²,

Chen³

et al. 2009

Czech J. Food Sci.

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show abstract

“…However, they have significant computational cost and complexity. Simpler and more suitable algorithms for on-line SHM make use of Least Squares Estimation (LSE) [3,[9][10][11][12][13][14] with different stochastic gradient estimation approaches.…”

Section: Introductionmentioning

confidence: 99%

LMS-based approach to structural health monitoring of nonlinear hysteretic structures

Nayyerloo

Chase

MacRae

et al. 2010

Structural Health Monitoring

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Structural health monitoring (SHM) algorithms based on adaptive least mean squares (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real-time in a computationally efficient fashion. However, better metrics of seismic structural damage and future utility after an event are related to permanent and total plastic deformations. This study presents a modified LMS-based SHM method and a novel two-step structural identification technique using a baseline nonlinear Bouc—Wen structural model to directly identify changes in stiffness due to damage as well as plastic or permanent deflections. The algorithm is designed to be computationally efficient; therefore it can work in real-time. An in silico single-degree-of-freedom (SDOF) nonlinear shear-type structure is used to prove the concept. The efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections is assessed under different ground motions using a suite of 20 different ground acceleration records. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed LMS-based SHM algorithm identifies stiffness changes to within 10% of true values within 2s. Permanent deflection is identified to within 14% of the actual as-modeled value using noise-free simulation-derived structural responses. This latter value provides important post-event information on the future serviceability, safety, and repair cost.

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“…Displacements can be measured with 1-3 mm accuracy for rates of up to 3-4 Hz, which include the modal frequencies of the rigid structure [16], and for 1 Hz, measurement errors have been stated as less than 12% [9]. The use of GPS opens up new opportunities in real-time structural health monitoring [15].…”

mentioning

confidence: 99%

“…Significant changes in story stiffness are often required as well, which would normally cause clearly visible damage [15]. Equally, [13,16] identified changes in structural stiffness in real-time using a Least Mean Squared (LMS)-based adaptive filtering approach in real-time. However, this method requires measurement of velocity and displacement, which has often been considered impractical in many realistic cases due to excessive sensor requirements.…”

mentioning

confidence: 99%

“…Recently there have been significant advances in Global Positioning System (GPS) displacement monitoring technology for large structures [15][16][17][18][19][20]. Displacements can be measured with 1-3 mm accuracy for rates of up to 3-4 Hz, which include the modal frequencies of the rigid structure [16], and for 1 Hz, measurement errors have been stated as less than 12% [9].…”

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confidence: 99%

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Survey and Introduction to the Focused Section on Mechatronics for Sustainable and Resilient Civil Infrastructure

Wang

Chase

et al. 2013

IEEE/ASME Trans. Mechatron.

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Efficient structural health monitoring for a benchmark structure using adaptive RLS filters

Cited by 32 publications

References 16 publications

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

LMS-based approach to structural health monitoring of nonlinear hysteretic structures

Survey and Introduction to the Focused Section on Mechatronics for Sustainable and Resilient Civil Infrastructure

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