Modal assurance distribution of multivariate signals for modal identification of time-varying dynamic systems

Quqa, Said; Landi, Luca; Diotallevi, Pier Paolo

doi:10.1016/j.ymssp.2020.107136

Cited by 27 publications

(26 citation statements)

References 51 publications

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“…The MAD is thus a map in the time‐frequency plane that indicates the “similarity” of neighboring ODSs obtained by applying the WPT to the channels of a multivariate signal consisting of structural responses. In the original formulation, 25 the MAD is obtained for long‐term applications to linear time‐varying structures. In this paper, the concept behind this technique is exploited to obtain a novel formulation of the MAD applicable to the response of nonlinear systems even for short‐term applications, such as the analysis of the structural behavior during earthquakes.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…The instantaneous identification of resonant components in this situation enables the extraction of a time‐domain description of NNMs during the structural nonlinear excursion, which may provide valuable information for the evaluation of the structural integrity in the aftermath of a seismic event. However, due to the significant differences that exist between the long‐term linear case 25 and the short‐term nonlinear situation addressed in this study, a novel identification algorithm is proposed hereinafter.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…The MAD is a TFR of multivariate and multicomponent signals used to highlight the presence of different modal responses based on the analysis of signal subbands obtained through a preliminary time‐frequency decomposition. In the original formulation, 25 the decomposition of each channel of the multivariate signal is performed through the WPT, which enables the extraction of nonredundant wavelet coefficients representative of localized (in frequency and time) dynamic features. For each time instant

t

and frequency subband

k

(with

k = 1, \dots, 2^{n}

, and

n

denoting the transform level), an instantaneous ODS

{bold-italicφ}_{k} false[t false]

can be evaluated considering the corresponding wavelet coefficients obtained from each data channel.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

“…Other developments of this method gave rise to reliable damage localization algorithms 24 . Recently, Quqa et al 25 . presented a novel TFR usable for the near‐real‐time visualization and extraction of instantaneous modal parameters of dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Seismic structural health monitoring using the modal assurance distribution

Quqa

Landi

Diotallevi

2021

Earthq Engng Struct Dyn

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Thanks to emerging techniques in the field of signal processing and due to improvements in smart sensing systems which enable the event‐triggered acquisition of high‐fidelity data at the occurrence of strong ground motion events, seismic structural health monitoring has grown considerably in the last few decades. In this paper, the modal assurance distribution, an alternative time‐frequency representation of the modal features of multivariate and multicomponent signals, is extended for application to short‐term nonstationary vibrational structural responses in which the system may manifest its nonlinear behavior. A general procedure for the extraction of the decoupled normal modes is presented, which allows the identification of instantaneous modal parameters in order to investigate in detail the structural behavior during earthquakes. Valuable information that cannot be recovered by means of traditional criteria can thus be exploited for accurate damage assessment. The results obtained for two case studies consisting of a numerical model with softening nonlinear behavior and a full‐scale experimental reinforced concrete benchmark show the potential and applicability of the method proposed for the integrity assessment of civil structures.

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Section: Modal Assurance Distributionmentioning

confidence: 99%

Section: Modal Assurance Distributionmentioning

confidence: 99%

t

and frequency subband

k

(with

k = 1, \dots, 2^{n}

, and

n

denoting the transform level), an instantaneous ODS

{bold-italicφ}_{k} false[t false]

can be evaluated considering the corresponding wavelet coefficients obtained from each data channel.…”

Section: Modal Assurance Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Seismic structural health monitoring using the modal assurance distribution

Quqa

Landi

Diotallevi

2021

Earthq Engng Struct Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…The time-frequency methods for non-stationary signal analysis studied in the literature include Wigner-Ville distribution (WVD), short-time Fourier transform (STFT), wavelet transform (WT), and Hilbert transform methods. [7][8][9][10][11][12][13][14] One of the widely used time-frequency analysis techniques is the Hilbert transform-based method. For instance, Hilbert-Huang transform (HHT) has been applied for non-stationary signal analysis in many engineering fields.…”

Section: Introductionmentioning

confidence: 99%

Enhanced vibration decomposition method based on multisynchrosqueezing transform and analytical mode decomposition

Hao

2021

Struct Control Health Monit

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Instantaneous identification of densely instrumented structures using line topology sensor networks

Quqa

Landi

Diotallevi

2021

Structural Contr & Hlth

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Summary In this paper, a new strategy for vibration‐based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star‐topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real‐time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures.

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Modal assurance distribution of multivariate signals for modal identification of time-varying dynamic systems

Cited by 27 publications

References 51 publications

Seismic structural health monitoring using the modal assurance distribution

Seismic structural health monitoring using the modal assurance distribution

Enhanced vibration decomposition method based on multisynchrosqueezing transform and analytical mode decomposition

Instantaneous identification of densely instrumented structures using line topology sensor networks

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