Corrosion Type Identification in Flanged Joints Using a Novel Recurrent Neural Network Technique Applied to Electrochemical Noise Measurements

A corrosion monitoring apparatus, comprising:

• at least one electrochemical noise (EN) measurement device for measuring raw EN data comprising electrochemical current noise (ECN) data and raw electrochemical potential noise (EPN) data from EN signals of the metallic structure;
• a processor;
• and
• a non-transitory computer-readable medium storing program code for a method of automatically classifying or characterizing types of corrosion of a metallic structure using electrochemical noise (EN) data without requiring prior labeling or human supervision, wherein the program code, when executed by one or more processors, causes the processors to:
• receiving raw EN data measured by the at least one EN measurement device corresponding to the metallic structure;
• determining input data sequences by dividing the received EN data into a plurality of sequences;
• inputting the input data sequences into an autoencoder for automatically determining corrosion-related temporal features present in the input data sequences;
• and
• using the corrosion-related temporal features automatically determined by the autoencoder to classify or characterize at least one type of corrosion of the metallic structure based on the inputted data sequences.

A method of automatically classifying or characterizing types of corrosion of a metallic structure or bolted flanged joint using electrochemical noise (EN) data without requiring prior labeling or human supervision, the method comprising:

• receiving EN data corresponding to the metallic structure or to the bolted flanged joint, wherein the received EN data comprises electrochemical current noise data and electrochemical potential noise data acquired using EN measurement;
• determining input data sequences by dividing the received EN data into a plurality of sequences;
• inputting the input data sequences into an autoencoder for automatically determining corrosion-related temporal features present in the input data sequences;
• and
• using the corrosion-related temporal features automatically determined by the autoencoder to classify or characterize at least one type of corrosion of the metallic structure or of the bolted flanged joint based on the inputted data sequences.