Scale transform signal processing for optimal ultrasonic temperature compensation

Harley, Joel B.; Moura, J.M.F.

doi:10.1109/tuffc.2012.2448

Cited by 102 publications

(117 citation statements)

References 34 publications

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“…where α(T ) is a stretch-time coefficient or scale factor (Harley and Moura, 2012). Based on these assumptions, the temperature effect in the signal can be simplified as:…”

Section: Modelmentioning

confidence: 99%

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A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

Fendzi

Rébillat

Mechbal

et al. 2016

Structural Health Monitoring

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This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phaseshift in signals caused by temperature changes. An ordinary least square (OLS) algorithm is used to estimate these unknown parameters. After estimating these parameters at each temperature in the operating range, linear functional relationships between the temperature and the estimated parameters are derived using the least squares method. A temperature compensation model is developed based on this linear relationship that allows one to reconstruct sensor signals at any arbitrary temperature. The proposed approach is validated numerically and experimentally for an anisotropic composite plate at different temperatures ranging from 16• C to 85 • C. A close match is found between the measured signals and the reconstructed ones. This approach is interesting as it needs only a limited set of piezo-sensor signals at different temperatures for model training and temperature compensation at any arbitrary temperature. Damage localization results after temperature compensation demonstrate its robustness and effectiveness.

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“…where α(T ) is a stretch-time coefficient or scale factor (Harley and Moura, 2012). Based on these assumptions, the temperature effect in the signal can be simplified as:…”

Section: Modelmentioning

confidence: 99%

“…In practical applications these important requirements are not always available. Unlike the OBS, the BSS compensation techniques seek to build a model of the effects of temperature change on wave signals (Harley and Moura, 2012;Michaels and Michaels, 2005). The advantage of this technique is that it requires few baseline signals.…”

Section: Introductionmentioning

confidence: 99%

A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

Fendzi

Rébillat

Mechbal

et al. 2016

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…The first step is to align the frontwalls of the baseline and current signals in order to eliminate the temperature influences on the wave propagation in water; the alignment is done by cross correlating the frontwall signals. Then, a scale-transform-based stretch method [15] is used to compensate for the wave speed change in the metal.…”

Section: Temperature Compensation Methodsmentioning

confidence: 99%

“…Thus, without compensating for these effects, raw subtraction of two signals obtained in different situations can be ineffective in enhancing SNR or even make it worse. In this study, a shift and scale-transform-based stretch [15] combined method is used for temperature compensation and a frequency compensation method based on frontwall reflection spectra is developed to compensate for the transducer frequency response change.…”

Section: Introductionmentioning

confidence: 99%

Feasibility and Reliability of Grain Noise Suppression in Monitoring of Highly Scattering Materials

Liu

Pamel

et al. 2017

J Nondestruct Eval

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A feasibility study on grain noise suppression using baseline subtraction is presented in this paper. Monitoring is usually done with permanently installed transducers but this is not always possible; here instead monitoring is conducted by carrying out repeat C-scans and the feasibility of grain noise suppression by subtracting A-scans extracted from the C-scans is investigated. The success of this technique depends on the ability to reproduce the same conditions for each scan, including a consistent stand-off, angle, and lateral position of the transducer relative to the testpiece. The significance of errors are illustrated and a 3D cross correlation is used which enables the same lateral position to be located within successive C-scans. The experimental results show that a noise reduction of around 15 dB is obtained after baseline subtraction, which will significantly improve the defect detection sensitivity. In practice however, successive C-scans may be conducted at different temperatures and with different transducers of similar specifications but a varying frequency response. Compensation techniques to reduce the impact of such variations are then presented and their effectiveness is verified experimentally. It is shown that it is feasible to obtain an overall improvement of around 10 dB in the signal to noise ratio via baseline subtraction, where a temperature difference of up to 10 • C and a peak frequency shift of as much as ±250 kHz from a baseline value of around 7 MHz can be tolerated. However, this improvement was obtained in laboratory conditions with no changes to the surface of the specimen due to oxidation or corrosion. It is shown that differences in tem-B Yuan Liu y.liu14@imperial.ac.uk 1 NDE Group, Department of Mechanical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK perature and transducer frequency response are more difficult to compensate for than changes in test geometry and position.

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“…In this paper, we utilize stretch-based methods [30] and matrix decomposition based methods [31], [32] to reduce the effects of temperature on guided wave data. Stretch-based methods utilize an approximate model for temperature's effects on guided wave data to optimally fit each measurement to the baseline.…”

Section: Variabilitymentioning

confidence: 99%

Managing Complexity, Uncertainty, and Variability in Guided Wave Structural Health Monitoring

Harley

Liu

Oppenheim

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: Guided waves are an attractive tool for monitoring large structures. They are able to interrogate sizable areas at once, are sensitive to structural damage, and carry information about the damage location. Yet, there are many challenges to guided wave data analysis. First, guided waves are represented by a complex set of dispersive wave modes that distort their shape and phase as they travel through the medium. Second, the complex properties of guided waves are often unknown. Third, the unknown properties often vary as a function of time. As a result, to effectively analyze guided waves, their complex properties must be learned, tracked, and leveraged. This paper describes signal processing tools for managing the complexity, uncertainty, and variability of guided waves for structural health monitoring applications. We use a stretch-based and a matrix decomposition based temperature compensation methods to handle variability, sparse wavenumber analysis to learn the uncertain properties of guided waves, and matched field processing to then leverage these complexities. We demonstrate how these methods are combined for effective guided wave data analysis with experimental data from an aluminum plate under varying temperature conditions.

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Scale transform signal processing for optimal ultrasonic temperature compensation

Cited by 102 publications

References 34 publications

A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

Feasibility and Reliability of Grain Noise Suppression in Monitoring of Highly Scattering Materials

Managing Complexity, Uncertainty, and Variability in Guided Wave Structural Health Monitoring

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