Acoustic Emission for Corrosion Detection

2006 ◽  
pp. 231-236
Author(s):  
P.T. Cole ◽  
J.R. Watson
Keyword(s):  
Acoustic Emission ◽  
Corrosion Detection

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261040
Author(s):  
Zazilah May ◽  
M. K. Alam ◽  
Nazrul Anuar Nayan ◽  
Noor A’in A. Rahman ◽  
Muhammad Shazwan Mahmud
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Support Vector ◽  
Learning Approach ◽  
Severity Assessment ◽  
Corrosion Detection ◽  
Support Vector Classifier ◽  
Machine Learning Approach

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.

Acoustic Emission for Corrosion Detection

2006 ◽  
Vol 13-14 ◽  
pp. 231-236 ◽  
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.

The Corrosion Acoustic Emission Source Location Technique and its New Trend

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.

Early Corrosion Detection in Prestressed Concrete Girders Using Acoustic Emission

2014 ◽  
Vol 26 (3) ◽  
pp. 504-511 ◽  
Author(s):  
Mohamed K. ElBatanouny ◽  
Jesé Mangual ◽  
Paul H. Ziehl ◽  
Fabio Matta
Keyword(s):  
Acoustic Emission ◽  
Prestressed Concrete ◽  
Corrosion Detection ◽  
Concrete Girders ◽  
Prestressed Concrete Girders

scholarly journals Corrosion detection and severity level prediction using acoustic emission and machine learning based approach

2021 ◽  
Author(s):  
Muhammad Fahad Sheikh ◽  
Khurram Kamal ◽  
Faheem Rafique ◽  
Salman Sabir ◽  
Hassan Zaheer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Severity Level ◽  
Corrosion Detection

Propagation of acoustic emission signals in metallic fuselage structure

2001 ◽  
Vol 148 (4) ◽  
pp. 169-177 ◽  
Author(s):  
R.P. Dalton ◽  
P. Cawley ◽  
M.J. Lowe
Keyword(s):  
Acoustic Emission
Sign in / Sign up

Export Citation Format

Share Document