Processing and modeling dual-rate sampled data in seawater corrosion monitoring of low alloy steels

CHEN Liang; FU Dong-mei

doi:10.13374/j.issn2095-9389.2020.06.17.003

Volume 44 Issue 1

Jan. 2022

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CHEN Liang, FU Dong-mei. Processing and modeling dual-rate sampled data in seawater corrosion monitoring of low alloy steels[J]. Chinese Journal of Engineering, 2022, 44(1): 95-103. doi: 10.13374/j.issn2095-9389.2020.06.17.003

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CHEN Liang, FU Dong-mei. Processing and modeling dual-rate sampled data in seawater corrosion monitoring of low alloy steels[J]. Chinese Journal of Engineering, 2022, 44(1): 95-103. doi: 10.13374/j.issn2095-9389.2020.06.17.003

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Processing and modeling dual-rate sampled data in seawater corrosion monitoring of low alloy steels

doi: 10.13374/j.issn2095-9389.2020.06.17.003

CHEN Liang¹,
FU Dong-mei^{1, 2
,
,}

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: fdm_ustb@ustb.edu.cn
Received Date: 2020-06-17
Available Online: 2020-08-10
Publish Date: 2022-01-01

Abstract

Abstract

With the rapid development of Internet of Things technology, the use of front-end sensors realizes the corrosion potential online detection of low alloy steels in a marine environment, thereby obtaining multitudes of corrosion data. Concerning the problems of data information loss and modeling accuracy reduction caused by the use of the traditional mean value method when processing dual-rate corrosion data, a new dual-rate data processing and modeling algorithm combining the comprehensive index value (CIV) and improved relevance vector regression (IRVR) was proposed. First, the CIV was constructed to characterize the comprehensive influence of the input data, and the beetle antennae search (BAS) algorithm was applied to optimize its parameters. Then, linear regression models between the best CIV sequence and the output data were established to convert the dual-rate corrosion data into single-rate data for modeling, which retained more information of the original corrosion data. Finally, the IRVR method based on BAS optimization of compounding kernels was given to establish the prediction model for dual-rate seawater corrosion data of low alloy steels. The results show that the proposed model CIV-IRVR increases the number of modeling samples from 196 for the mean value method to 1834. Moreover, the mean absolute error, root mean square error, and coefficient of determination of the CIV-IRVR model are 1.1914 mV, 1.5729 mV, and 0.9963, respectively, which outperforms commonly used comparison algorithms, such as the artificial neural network (ANN) and support vector regression (SVR). Moreover, the CIV-IRVR model can help obtain the prediction results with error bars, and it has the absolute error distribution closest to 0, which highlights its excellent predictive performance on the seawater corrosion potential of low alloy steels. Thus, the proposed model not only reduces the information loss and improves the modeling accuracy but also has practical significance for modeling dual-rate seawater corrosion data.
- low alloy steels,
- seawater corrosion,
- dual-rate data,
- comprehensive index value,
- relevance vector regression

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References(26)

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