Compressive sensing for direct time of flight estimation in ultrasound-based NDT

R. Fuentes, K. Worden, I. Antoniadou, C. Mineo, S.G. Pierce, E.J. Cross

Research output: Contribution to conferencePaper

Abstract

This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.

Conference

Conference11th International Workshop on Structural Health Monitoring
Abbreviated titleIWSHM 2017
CountryUnited States
CityStanford
Period12/09/1714/09/17
Internet address

Fingerprint

Nondestructive examination
Ultrasonics
Autocorrelation
autocorrelation
structural health monitoring
Structural health monitoring
ultrasonics
estimates
matrices

Keywords

  • time-of-flight (TOF)
  • ultrasound pulses
  • non destructive testing
  • NDT
  • ultrasound signals

Cite this

Fuentes, R., Worden, K., Antoniadou, I., Mineo, C., Pierce, S. G., & Cross, E. J. (2017). Compressive sensing for direct time of flight estimation in ultrasound-based NDT. Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States.
Fuentes, R. ; Worden, K. ; Antoniadou, I. ; Mineo, C. ; Pierce, S.G. ; Cross, E.J. / Compressive sensing for direct time of flight estimation in ultrasound-based NDT. Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States.12 p.
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title = "Compressive sensing for direct time of flight estimation in ultrasound-based NDT",
abstract = "This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.",
keywords = "time-of-flight (TOF), ultrasound pulses, non destructive testing, NDT, ultrasound signals",
author = "R. Fuentes and K. Worden and I. Antoniadou and C. Mineo and S.G. Pierce and E.J. Cross",
year = "2017",
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language = "English",
note = "11th International Workshop on Structural Health Monitoring, IWSHM 2017 ; Conference date: 12-09-2017 Through 14-09-2017",
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Fuentes, R, Worden, K, Antoniadou, I, Mineo, C, Pierce, SG & Cross, EJ 2017, 'Compressive sensing for direct time of flight estimation in ultrasound-based NDT' Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States, 12/09/17 - 14/09/17, .

Compressive sensing for direct time of flight estimation in ultrasound-based NDT. / Fuentes, R.; Worden, K.; Antoniadou, I.; Mineo, C.; Pierce, S.G.; Cross, E.J.

2017. Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Compressive sensing for direct time of flight estimation in ultrasound-based NDT

AU - Fuentes, R.

AU - Worden, K.

AU - Antoniadou, I.

AU - Mineo, C.

AU - Pierce, S.G.

AU - Cross, E.J.

PY - 2017/9/12

Y1 - 2017/9/12

N2 - This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.

AB - This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.

KW - time-of-flight (TOF)

KW - ultrasound pulses

KW - non destructive testing

KW - NDT

KW - ultrasound signals

M3 - Paper

ER -

Fuentes R, Worden K, Antoniadou I, Mineo C, Pierce SG, Cross EJ. Compressive sensing for direct time of flight estimation in ultrasound-based NDT. 2017. Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States.