MACHINE LEARNING BASED TECHNIQUES FOR PREDICTING COMPONENT CORROSION LIKELIHOOD

A machine learning based method for determining a likelihood of corrosion of a component is provided. The method comprises receiving data associated with a portion of at least one component, the data describing one or more operating conditions of the portion of the at least one component, applying,...

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Bibliographic Details
Main Authors Kim, Okja, Chan, Godine Kok Yan, Borogovac, Tarik, Boriah, Shyam, Esfe, Mohamad Bagheri
Format Patent
LanguageEnglish
Published 02.03.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A machine learning based method for determining a likelihood of corrosion of a component is provided. The method comprises receiving data associated with a portion of at least one component, the data describing one or more operating conditions of the portion of the at least one component, applying, to the data associated with the portion, a first machine learning model, determining, responsive to the applying of the first machine learning model, a likelihood of corrosion specific to the at least one component based at least in part on the one or more operating conditions of the portion, and outputting, automatically and without user intervention, the likelihood of corrosion specific to the at least one component on a display.
Bibliography:Application Number: US202217878511