The use of skewness, kurtosis and neural networks for determining corrosion mechanism from electrochemical noise data
- Reid (S.), Lymm (United Kingdom)
- M.J. Schiff and Associates, Inc., Claremont, CA (United States)
- Lockheed Martin Hanford Co., Richland, WA (United States)
This 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 pre-processing 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.
- OSTI ID:
- 352575
- Report Number(s):
- CONF-980316--
- Country of Publication:
- United States
- Language:
- English
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