Time-Varying System Identification Using a Hybrid Blind Source Separation Method

Musafere, F.; Sadhu, Ayan; Liu, K.

doi:10.1007/978-3-319-29956-3_11

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Musafere, A. Sadhu, and K. Liu proposed a method with recourse to the framework of blind source separation (BSS) to detect damage time and severity. Other time-frequency decompositions, such as Hilbert transform and time-varying auto-regressive modeling were investigated to enhance source separation capability of the BSS method [12].…”

Section: Introductionmentioning

confidence: 99%

Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations

Esmaielzadeh¹,

Ahmadi²,

Hosseini³

2019

J. vibroeng.

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In structural engineering, damage detection in concrete gravity dams (CGDS) is a practical problem. Dam destruction can have severe financial consequences and may even lead to fatalities. Therefore, structural health monitoring in advance is crucial. In this regard, a well-known CGD, namely the Pine Flat Dam, has been chosen for the Finite Element Modeling. In this paper, damage is induced in the dam neck through elasticity modulus reduction by 40 % and 80 %. In addition, after applying Northridge earthquake, the acceleration in structure nodes for intact and damaged cases are recorded in vector formats. Using various methods, such as Discrete-time Fourier Transform (DTFT), Wavelet transform and Wiener transform, the differences between these two signals are investigated. The standard deviation (S.D.) of variations is chosen as the performance metric and is applied to the signal amplitude between intact and damage observations/signals. The reason why several signal processing algorithms are used is finding an approach which shows more clearly the differences caused by the destruction. This is evaluated via S.D. values for different algorithms. The results confirm the superiority of DTFT over other given algorithms. DTFT has a negligible outperformance (approximately zero dB) with respect to the Wavelet transform in both the crest and the lower nodes of the dam. This rate for DTFT and Wavelet is 10dB higher than that of Wiener and 35 dB in comparison with the simple amplitude difference. Moreover, the detection thresholds for the given methods are compared, and it is verified that the DTFT and Wavelet indicate the best performance.

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Section: Introductionmentioning

confidence: 99%

Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations

Esmaielzadeh¹,

Ahmadi²,

Hosseini³

2019

J. vibroeng.

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“…STSM models such as AR (autoregressive), MA (moving average), or ARMA (autoregressive moving average), among others, are used mainly to model the behavior of time signals with linear or time-invariant properties; however, they present problems when modeling noisy and non-linear (NL) structural responses, which are generally measured in structural responses [37]. In order to lessen the problems encountered when using the aforementioned techniques, other SPS are employed for monitoring structure conditions, such as wavelet transform (WT) [2,[38][39][40][41][42][43] and blind source separation (BSS) [44,45]. In particular, WT is a TF strategy designed to handle non-linear and non-stationary signals.…”

Section: Introductionmentioning

confidence: 99%

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

et al. 2018

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Truss-type designs are widely used in civil structures. Despite the fact that they are robust and reliable structures, different kinds of damage can appear. In order to avoid human and economic losses, the development and application of damage-detection methodologies are paramount. In this work, a methodology based on the empirical mode decomposition (EMD) method and the Shannon Entropy Index (SEI) to detect incipient damages associated with corrosion in a 3D 9-bay truss-type bridge is presented. As different EMD methods are presented in literature, the most representative methods are investigated in order to evaluate their performance for this task. To this end, the vibration signals generated in the truss-type bridge at different conditions are analyzed. For the damage condition, four severity levels of simulated corrosion (1 mm, 3 mm, 5 mm, and 8 mm of diameter reduction) generated into the elements of truss-type bridge are considered. Results demonstrate the effectiveness of the proposal in terms of detecting corrosion in its very early stage (1 mm of reduction in the element).

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Application of signal processing techniques in structural health monitoring of concrete gravity dams

2023

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Time-Varying System Identification Using a Hybrid Blind Source Separation Method

Cited by 4 publications

References 27 publications

Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations

Damage detection in concrete gravity dams using signal processing algorithms based on earthquake vibrations

EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure

Application of signal processing techniques in structural health monitoring of concrete gravity dams

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