Application of Machine Learning for Data with an Atmospheric Corrosion Monitoring Sensor Based on Strain Measurements

Author:

Okura Taisei,Kasai NaoyaORCID,Minowa Hirotsugu,Okazaki ShinjiORCID

Abstract

Machine learning methods were applied to data with an atmospheric corrosion monitoring sensor based on strain measurements to improve the evaluation accuracy of the thickness reduction of a low-carbon steel plate due to atmospheric corrosion. Monitoring data used in this study were taken in a previous study using active–dummy strain gauges for corrosion product experiments. Values measured by the gauges before inducing corrosion via saltwater treatment of the test piece and reference data of the thickness reduction in a reference test piece were used for training data. By using the trained machine learning methods, the errors for the outputs of the machine learning models were smaller than those for the evaluation in monitoring data of our previous study.

Publisher

MDPI AG

Subject

General Materials Science,Metals and Alloys

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extraction of atmospheric corrosion monitoring sensor signals using MSSA and corrosion progress prediction with an LSTM model;Sensors and Actuators A: Physical;2024-02

2. Prediction performance on each interpolation and extrapolation for corrosion rates with laws using machine learning;2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI);2023-07-08

3. Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation;2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA);2022-12

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