Identifying corrosion of carbon steel buried in iron ore and coal cargoes based on recurrence quantification analysis of electrochemical noise

2018 ◽  
Vol 283 ◽  
pp. 212-220 ◽  
Author(s):  
Y. Hou ◽  
C. Aldrich ◽  
K. Lepkova ◽  
B. Kinsella
Keyword(s):  
Carbon Steel ◽  
Iron Ore ◽  
Electrochemical Noise ◽  
Recurrence Quantification Analysis ◽  
Recurrence Quantification ◽  
Quantification Analysis

scholarly journals Use of electrochemical current noise method to monitor carbon steel corrosion under mineral wool insulation

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Yang Hou ◽  
Thunyaluk Pojtanabuntoeng ◽  
Mariano Iannuzzi
Keyword(s):  
Weight Loss ◽  
Carbon Steel ◽  
Steel Corrosion ◽  
Noise Signal ◽  
Mineral Wool ◽  
Recurrence Quantification Analysis ◽  
Current Noise ◽  
Recurrence Quantification ◽  
Electrochemical Current ◽  
Quantification Analysis

AbstractCorrosion of carbon steel under mineral wool insulation was studied using the electrochemical current noise (ECN) method. Intensities of corrosion were validated using gravimetry, and the form of corrosion confirmed using optical microscopy. The standard deviation of the current noise signal agreed with weight loss results and was demonstrated as a reliable indicator of the degree of corrosion under mineral wool insulation. Recurrence quantification analysis was used to extract feature variables from ECN signals, which were later used to develop a random forest model to identify the type of corrosion, i.e., uniform or localised corrosion. The trained model was successfully applied to predict the extent of localised corrosion associated with mineral wool insulation.

RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROCHEMICAL NOISE DATA DURING PIT DEVELOPMENT

2007 ◽  
Vol 17 (10) ◽  
pp. 3725-3728 ◽  
Author(s):  
LUIS SANTOS MONTALBÁN ◽  
PÄIVI HENTTU ◽  
ROBERT PICHÉ
Keyword(s):  
Time Series ◽  
Pitting Corrosion ◽  
Electrochemical Noise ◽  
Recurrence Quantification Analysis ◽  
Corrosion Process ◽  
Current Time ◽  
Recurrence Quantification ◽  
Noise Data ◽  
Quantification Analysis ◽  
Aisi 316

Electrochemical noise (EN) data is commonly used to monitor corrosion of metals in various environments. In this work we use recurrence quantification analysis (RQA) to study EN time series of stainless steel AISI 316 samples immersed in a mildly corrosive electrolyte. It is found that RQA of current and potential time series reveal different information: current time series provides detailed information on the kinetics of the pitting corrosion process, while the potential time series identifies the transitions from one thermodynamic state to another in the pitting corrosion process.

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