Quantification of fatigue cracking in CT specimens with passive and active piezoelectric sensing

Yu, Jing; Ziehl, Paul; Zárate, Boris A.; Caicedo, Juan M.; Yu, Lingyu; Giurgiutiu, Victor; Metrovich, Brian; Matta, Fabio

doi:10.1117/12.847945

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Yu et.al. studied the noises that can be removed based on traditional filtering methods such as Swansong filters [5], [6]. The most basic level of filtering AE data is typically accomplished by setting a threshold for data acquisition, eliminating the low amplitude signals.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission signal filtering based on artificial intelligence

Inderyas¹,

Tayfur²,

Alver³

2022

eJNDT

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One of the factors affecting the performance of the acoustic emission method used for real-time monitoring of structures is the interference of noise signals with the damage-related AE signals. Since the signals detected from the sensors are analyzed to obtain damage information, noise-related signals cause inaccurate inspection results. Especially, in the conditions where electrical or mechanical friction is prominent, noise causes big data and errors in damage detection In this study, to solve this problem, it is aimed to develop an AE signal filtering model by artificial intelligence that can filter the noise-related signals. For this purpose, a wide range of AE data were filtered using conventional filtering techniques and noise- and damage-related signals were used to train artificial intelligence. The noise-related signals were successfully separated from the damage-related signals.

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Section: Introductionmentioning

confidence: 99%

Acoustic emission signal filtering based on artificial intelligence

Inderyas¹,

Tayfur²,

Alver³

2022

eJNDT

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“…Waveform features such as rise time, duration and amplitude were involved in filtering of the AE data [18][19][20]. The combination of pattern recognition and waveform feature analysis is suitable for both laboratory studies and field tests [21,22].…”

Section: Introductionmentioning

confidence: 99%

Stable and unstable fatigue prediction for A572 structural steel using acoustic emission

Ziehl

2012

Journal of Constructional Steel Research

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“…In the literature, dealing with the challenge of massive data due to high sensitivity of AE sensors and to long-term fatigue loading experiments, several processing approaches have been proposed [8,9,10]. In [8], it is considered that only signals with amplitude higher than 70 dB or recorded above 80% of peak load contain information related to damage mechanisms.…”

mentioning

confidence: 99%

“…In [8], it is considered that only signals with amplitude higher than 70 dB or recorded above 80% of peak load contain information related to damage mechanisms. In [9], "friction emission" tests in which the maximum cyclic load was decreased to a level that was insufficient to generate crack growth were performed to understand the AE signal characteristics arising from hydraulics, machine start/stop and slippage. All of the AE events at this lower peak load were therefore assumed to be due to friction emission.…”

mentioning

confidence: 99%

An unsupervised pattern recognition approach for AE data originating from fatigue tests on polymer–composite materials

Doan

Ramasso

Placet

et al. 2015

Mechanical Systems and Signal Processing

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This work investigates acoustic emission generated during tension fatigue tests carried out on a carbon fiber reinforced polymer (CFRP) composite specimen. Since fatigue data processing, especially noise reduction remains an important challenge in AE data analysis, a Mahalanobis distance-based noise modeling has been proposed in the present work to tackle this problem. A Davies-Bouldin-index-based sequential feature selection has been implemented for fast dimensionality reduction. A classifier offline-learned from quasi-static data is then used to classify the processed data to different AE sources with the possibility to dynamically accommodate with unseen ones. With an efficient proposed noise removal and automatic separation of AE events, this pattern discovery procedure provides an insight into fatigue damage development in composites in presence of millions of AE events.

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Quantification of fatigue cracking in CT specimens with passive and active piezoelectric sensing

Cited by 7 publications

References 23 publications

Acoustic emission signal filtering based on artificial intelligence

Acoustic emission signal filtering based on artificial intelligence

Stable and unstable fatigue prediction for A572 structural steel using acoustic emission

An unsupervised pattern recognition approach for AE data originating from fatigue tests on polymer–composite materials

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