Pitting Corrosion Monitoring Using Acoustic Emission

Prateepasen, Asa

doi:10.5772/33127

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Patil et al [ 3 ] designed a technique for evaluating accelerated corrosion tests based on AE. In addition, Prateepasen et al [ 4 ] utilized acoustic emission to detect pitting corrosion. Accelerated corrosion testing was performed on SS-304 specimens that had been ground with silicon carbide paper, rinsed with distilled water, and dried in the air in the proposed technique.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

et al. 2021

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Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

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Section: Introductionmentioning

confidence: 99%

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

et al. 2021

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“…Magnesium and its alloys, as well as many other metals, are disposed to pitting corrosion when exposed to environments containing chloride ions [6,9,10]. Pitting corrosion was documented to consist of three stages: (i) pit nucleation, (ii) development of metastable pits, which either repassivate or (iii) grow into stable ones, which further propagate in the material [11].…”

Section: Introductionmentioning

confidence: 99%

“…The AE technique has been successfully employed to monitor pitting and other types of corrosion (e.g. [10,11,[23][24][25][26][27][28][29][30][31]). For example, representative AE parameters effective for corrosion monitoring were identified to be the AE counts, rise time and frequency [24,29,11] or corrosion source recognition in the frequency domain was documented in [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…[10,11,[23][24][25][26][27][28][29][30][31]). For example, representative AE parameters effective for corrosion monitoring were identified to be the AE counts, rise time and frequency [24,29,11] or corrosion source recognition in the frequency domain was documented in [29][30][31]. The vast majority of works dealing with the AE monitoring of corrosion processes, however, focuses on stainless steels.…”

Section: Introductionmentioning

confidence: 99%

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Corrosion of pure magnesium and a WE43 magnesium alloy studied by advanced acoustic emission analysis

et al. 2018

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Corrosion behaviour of magnesium and WE43 magnesium alloy in 1 M NaCl aqueous solution was studied by potentiodynamic polarisation and advanced acoustic emission (AE) analyses. The AE parameters suitable for the monitoring of corrosion in magnesium were shown to be the AE counts, rise time, and duration. Moreover, the mechanisms of corrosion in these materials, i.e. the ion exchange, rupture of the protective film, and the pitting corrosion were identified and their evolution was followed by the novel Adaptive sequential k-means clustering technique. The presented methods can be also suitable for monitoring of different materials and for industrial applications.

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A Review on Non-destructive Techniques for Corrosion Monitoring in Reinforced Concrete Structures

Thakur

Saurav

2022

Lecture Notes in Civil Engineering

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Pitting Corrosion Monitoring Using Acoustic Emission

Cited by 6 publications

References 20 publications

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

Corrosion of pure magnesium and a WE43 magnesium alloy studied by advanced acoustic emission analysis

A Review on Non-destructive Techniques for Corrosion Monitoring in Reinforced Concrete Structures

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