Acoustic Emission for Corrosion Detection

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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Acoustic Emission for Corrosion Detection

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.13-14.231 ◽

2006 ◽

Vol 13-14 ◽

pp. 231-236 ◽

Cited By ~ 8

Author(s):

P.T. Cole ◽

J.R. Watson

Keyword(s):

Acoustic Emission ◽

Storage Tank ◽

Crack Formation ◽

Industrial Plant ◽

Reinforced Concrete Structures ◽

Passive Monitoring ◽

Corrosion Detection ◽

Process Plant ◽

Acoustic Methods ◽

Evaluation And Monitoring

Corrosion is the major cause of structural degradation in industrial plant and structures; the consequences of not identifying its presence and status can be severe, leading to a myriad of methods for its evaluation and monitoring. Amongst these there are a large number based on acoustic methods, and this paper concentrates on three variations involving passive monitoring with the aim of summarizing their usual area of applications and limitations. Passive monitoring involves listening to the process of corrosion itself, which usually causes acoustic emission as a result of the fracture and de-bonding of expansive corrosion products, localised yielding, or micro-crack formation. This method is applied to reinforced concrete structures, storage tank floors, and process plant whilst in service.

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The Corrosion Acoustic Emission Source Location Technique and its New Trend

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1167 ◽

2013 ◽

Vol 694-697 ◽

pp. 1167-1172

Author(s):

Hai Sheng Bi ◽

Zi Li Li ◽

Yuan Peng Cheng ◽

Isaac Isaac ◽

Jun Wang

Keyword(s):

Acoustic Emission ◽

Storage Tank ◽

Petrochemical Industry ◽

Source Location ◽

Location Method ◽

Corrosion Detection ◽

Emission Testing ◽

Acoustic Emission Source ◽

Acoustic Emission Testing ◽

Location Methods

The corrosion acoustic emission (AE) source location is one of the main purposes of acoustic emission testing (AET), corrosion detection and location can guarantee the safety and integrity of pipeline, storage tank and other equipment in the petrochemical industry. The computed source location and zonal location methods are reviewed in this paper, and also new source location method based on modal acoustic emission (MAE) is introduced and this new method will be more widely used in the field of corrosion detection in future.

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