Wavelet-transform-based method of analysis for Lamb-wave ultrasonic NDE signals

Legendre, S.; Massicotte, Daniel; Goyette, J.; Bose, Tanmoy

doi:10.1109/imtc.1999.776987

Cited by 15 publications

(10 citation statements)

References 7 publications

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“…Presence of cracks, delaminations or other defects will alter the shape of the wave produced by the PZT sensor with respect to a damage-free condition. Thus, it is possible to monitor the structure health condition by analyzing the differences between the two waves [1] [16]. An example of this is shown in Figure 1.…”

Section: Shm Methodsmentioning

confidence: 99%

Active sensing platform for wireless structural health monitoring

Musiani

Lin

Rosing

2007

Proceedings of the 6th International Conference on Information Processing in Sensor Networks - IPSN '07

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This paper presents SHiMmer, a wireless platform for active sensing that combines localized processing with energy harvesting to provide long-lived structural health monitoring. The life-cycle of the node is extended to 20 years by the use of super-capacitors for energy storage. During this period the node is expected to work completely maintenance-free. The node is capable of harvesting up to 780J per day. This makes it completely self-sufficient while employed in real structural health monitoring applications. Unlike other sensor networks that periodically monitor a structure and route information to a base station, our device acquires the data and processes it locally after being radio-triggered by an external agent. The localized processing allows us to avoid issues due to network congestion. Our experiments show that its 32-bits computational core can run at 100MIPS for 15 minutes daily.

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Section: Shm Methodsmentioning

confidence: 99%

Active sensing platform for wireless structural health monitoring

Musiani

Lin

Rosing

2007

Proceedings of the 6th International Conference on Information Processing in Sensor Networks - IPSN '07

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“…For nonstationary ultrasonic signals, WT outperforms FT due to its ability to adapt the window size of the processed signal. Therefore, one can easily separate information and noise without needing a complex windowing step [15]. However, the main shortcoming of WT is that the time localization is poor for low frequency signals and the frequency resolution is poor for high frequency signals.…”

Section: The Basic Principle Of Emdmentioning

confidence: 99%

Empirical Mode Decomposition and Rough Set Attribute Reduction for Ultrasonic Flaw Signal Classification

Yang¹,

Yang²

2014

IJCIS

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Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is used to obtain the intrinsic mode functions (IMFs) of original signal, and their corresponding traditional time and frequency domain based statistical parameters are extracted as the initial features. After that, spectral clustering method is used for feature value discretization so that rough set attribute reduction (RSAR) can be applied to implement feature selection. The final features are taken as input of artificial neural networks (ANNs) to train the decision classifier for flaw identification. Experimental results show that compared to conventional wavelet transform based schemes and principal components analysis, EMD combined with RSAR can improve the performance of feature extraction and selection. ANN by using such scheme can effectively classify different ultrasonic flaw signals with high accuracy and low training elapsed time.

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“…It is worth to mention that, in the most general case where e n (t) and e p (t) are taken into account simultaneously, one should first try to remove almost completely e n (t) and then apply more sophisticated denoising methods (wavelet filtering, independent component analysis, etc. [8,27,40]) to optimally separate the desired signal components from the unwanted one e p (t).…”

Section: A Minimal Channel Modelmentioning

confidence: 99%

Excitation and Deconvolution in Ultrasound Nondestructive Testing Systems

Caporale

Callegari

Hutchins

et al. 2014

Ultrasonic Nondestructive Evaluation Systems

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The pulse-echo (PuE) model based on short pulses is introduced as a starting point. Its limits in terms of signal-to-noise ratio (SNR) are reviewed, together with the tradeoff between the excitation energy and the time resolution. It is shown how signal processing can improve the overall performance in terms of system sensitivity and resolution as long as the exciting waveform is chosen taking into account the system characteristics and is in agreement with the adopted processing techniques. The degrees of freedom on which one can operate are the time evolution law of the excitation signal and the processing technique adopted on the receiving end. In their exploitation, the designer should consider the physical characteristics of the hardware devices being employed in order to optimize the overall performance. The importance of introducing merit factors is discussed. By offering a quantitative measure of system performance, they are indispensable tools to drive formal optimization S. Caporale ( )

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Wavelet-transform-based method of analysis for Lamb-wave ultrasonic NDE signals

Cited by 15 publications

References 7 publications

Active sensing platform for wireless structural health monitoring

Active sensing platform for wireless structural health monitoring

Empirical Mode Decomposition and Rough Set Attribute Reduction for Ultrasonic Flaw Signal Classification

Excitation and Deconvolution in Ultrasound Nondestructive Testing Systems

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