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96509 FEASIBILITY OF USING ARTIFICIAL NEURAL NETWORKS WITH E. I. S. DATA FROM COATED STEEL

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Product Number: 51300-96509-SG
ISBN: 96509 1996 CP
Author: Harvey P. Hack, M. Allen Matteson
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Elcctrochcmical impedance spectroscopy (E. I. S.) techniques can provide information about the condition of protective coatings on steel marine structures. Currently, an expert is required to interpret the data produced from an E. I. S. measurement. classifying the coating as good or poor, or identifying the data as bad. This limits the use of E. I. S, techniques to experienced operators. If the E. I. S. technique is to be used for production by inexperienced operators. measurements must be classified automatically, This investigation uses artificial neural networks (ANN) to develop an automated E. I. S. data classifier. ANN’s were trained with a large data base of measurements on known good or poor coatings, including some bad data. The ANN’s were tested with E. I. S. data not included in the training set. A variety of measurement signal processing schemes and network structures were evaluated, ANN’s were devcloped which can accurately determine if the coating is good or poor, and whether measurement problems produced bad data. Keywords: neural network, electrochemical impedance spectroscopy, E. I. S., A.C. Impedance, organic coating, seawater
Elcctrochcmical impedance spectroscopy (E. I. S.) techniques can provide information about the condition of protective coatings on steel marine structures. Currently, an expert is required to interpret the data produced from an E. I. S. measurement. classifying the coating as good or poor, or identifying the data as bad. This limits the use of E. I. S, techniques to experienced operators. If the E. I. S. technique is to be used for production by inexperienced operators. measurements must be classified automatically, This investigation uses artificial neural networks (ANN) to develop an automated E. I. S. data classifier. ANN’s were trained with a large data base of measurements on known good or poor coatings, including some bad data. The ANN’s were tested with E. I. S. data not included in the training set. A variety of measurement signal processing schemes and network structures were evaluated, ANN’s were devcloped which can accurately determine if the coating is good or poor, and whether measurement problems produced bad data. Keywords: neural network, electrochemical impedance spectroscopy, E. I. S., A.C. Impedance, organic coating, seawater
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