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98176 THE USE OF SKEWNESS, KURTOSIS AND NEURAL NETWORKS FOR DETERMINING CORROSION MECHANISM FROM ELECTROCHEMICAL NOISE DATA

Picture for 98176 THE USE OF SKEWNESS, KURTOSIS AND NEURAL
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Product Number: 51300-98176-SG
ISBN: 98176 1998 CP
Author: S. Reid, G. E. C. Bell, G. L. Edgemon
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Thk paper describes the work undertaken to de-skill the complex procedure of determining corrosion mechanisms derived from electrochemical noise data. The use of neural networks is discussed and applied to the real time generated electrochemical noise data files with the purpose of determining characteristics particular to individual types of corrosion mechanisms. The electrochemical noise signals can have a wide dynamic range and various methods of raw data preprocessing prior to neural network analysis were investigated. Normalized data were ultimately used as input to the final network analysis. Various network schemes were designed, trained and tested. Factors such as the network learning schedule and network design were considered before a final network was implemented to achieve a solution. Neural networks trained using general and localized corrosion data from various material environment systems were used to analyze data from simulated nuclear waste tank environments with favorable results. Keywords: Electrochemical Noise, Neural Networks, Data Analysis, Skewness and Kurtosis
Thk paper describes the work undertaken to de-skill the complex procedure of determining corrosion mechanisms derived from electrochemical noise data. The use of neural networks is discussed and applied to the real time generated electrochemical noise data files with the purpose of determining characteristics particular to individual types of corrosion mechanisms. The electrochemical noise signals can have a wide dynamic range and various methods of raw data preprocessing prior to neural network analysis were investigated. Normalized data were ultimately used as input to the final network analysis. Various network schemes were designed, trained and tested. Factors such as the network learning schedule and network design were considered before a final network was implemented to achieve a solution. Neural networks trained using general and localized corrosion data from various material environment systems were used to analyze data from simulated nuclear waste tank environments with favorable results. Keywords: Electrochemical Noise, Neural Networks, Data Analysis, Skewness and Kurtosis
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