Linear signal processing methods and decay ratio estimation

Prieto‐Guerrero, Alfonso; Espinosa-Paredes, Gilberto

doi:10.1016/b978-0-08-102445-4.00006-0

Cited by 8 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These three options have all the advantages of being analytic; that is to say, they are complex-valued and their Fourier transforms are supported only on the positive real axis of the frequency domain. Since these complex wavelets respond solely to the non-negative frequencies for any given signal, their transform will have a less oscillatory modulus than in the case of a real-valued wavelet [46].…”

Section: Candidate Time-scale Transformsmentioning

confidence: 99%

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Civera

Surace

2022

Buildings

View full text Add to dashboard Cite

Damage assessment techniques based on entropy measurements have been recently proposed for the structural health monitoring of civil structures and infrastructures. A quasi-real-time approach, based on the use of instantaneous spectral entropy (ISE) over an uninterrupted stream of data, is discussed here. The methodology is proposed for the detection of sudden damage-related structural changes (more specifically, linear stiffness reductions and nonlinear breathing cracks). The method operates by framing the continuous stream of vibration signals and comparing the single frames to a known baseline. The approach is also suitable for nonstationary signals originating from nonlinearly behaving structures. The procedure is validated on an experimental benchmark: a laboratory-scaled model of a three-storey single-span frame metallic structure. Three different definitions of entropy and six candidate time–frequency/time-scale transforms have been tested to find the optimal settings.

show abstract

Section: Candidate Time-scale Transformsmentioning

confidence: 99%

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Civera

Surace

2022

Buildings

View full text Add to dashboard Cite

show abstract

“…These three options have all the advantage of being analytic; that is to say, they are complexvalued, their Fourier transforms are supported only on the positive real axis of the frequency domain. Since these complex wavelets respond solely to the nonnegative frequencies for any given signal, their transform will have a less oscillatory modulus than in the case of real-valued wavelet [41]. This advantageous property enables better detecting and tracking capabilities for instantaneous signal parameters.…”

Section: Candidate Time-scale (Ts) Transformsmentioning

confidence: 99%

The Instantaneous Spectral Entropy for Real-time, Online Structural Health Monitoring.

Civera

Surace

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Entropy measurements have been recently proposed for the damage assessment of civil structures and mechanical systems. Here, a quasi-real-time approach, based on Instantaneous Spectral Entropy (ISE), is proposed for the detection of sudden stiffness reduction, breathing cracks, and other kinds of structural changes. The method, validated on an experimental benchmark, is suitable for nonstationary signals originating from nonlinear structures as well.

show abstract

Efficient Estimation of Empirical Green’s Function by Removing Transients From Sensor Data Using Time–Frequency Normalization Technique

Gupta

Mukhopadhyay

2022

IEEE Sensors J.

View full text Add to dashboard Cite

Linear signal processing methods and decay ratio estimation

Cited by 8 publications

References 26 publications

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

Instantaneous Spectral Entropy: An Application for the Online Monitoring of Multi-Storey Frame Structures

The Instantaneous Spectral Entropy for Real-time, Online Structural Health Monitoring.

Efficient Estimation of Empirical Green’s Function by Removing Transients From Sensor Data Using Time–Frequency Normalization Technique

Contact Info

Product

Resources

About