An Electrochemical Noise Signal Recognition Method for Corrosion Monitoring

doi:10.11902/1005.4537.2024.336

Journal of Chinese Society for Corrosion and protection

2025, Vol. 45

Issue (5): 1433-1440 DOI: 10.11902/1005.4537.2024.336

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An Electrochemical Noise Signal Recognition Method for Corrosion Monitoring

SHEN Zhiyuan¹^,², SHAN Guangbin¹^,², CHEN Mindong¹^,²(

), LIU Yuanshuang¹^,²

1 State Key Laboratory of Chemical Safety, Qingdao 266104, China
2 SINOPEC Research Institute of Safety Engineering Co., Ltd., Qingdao 266104, China

Cite this article:

SHEN Zhiyuan, SHAN Guangbin, CHEN Mindong, LIU Yuanshuang. An Electrochemical Noise Signal Recognition Method for Corrosion Monitoring. Journal of Chinese Society for Corrosion and protection, 2025, 45(5): 1433-1440.

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Abstract

An electrochemical noise signal recognition network for corrosion monitoring was proposed in order to realize the automatic and accurate analysis of electrochemical noise signals collected in real time. The maximum pooling operation method was adopted to smooth the signal while the details and trend features of the signal are preserved, and the end-to-end training is realized by using a network module design. Based on residual structures and spatial pyramid pooling structures, a feature extraction module was designed to enhance the network's ability to characterize the key features. The model was trained and tested via 5-fold cross-validation based on the experimentally acquired electrochemical noise signals. The results show that the proposed model achieved an overall accuracy and F1 score of 0.9463 and 0.9282, respectively, demonstrating that neural networks can be used to accurately identify electrochemical noise signals.

Key words: electrochemical noise neural network deep learning pattern recognition

Received: 12 October 2024 32134.14.1005.4537.2024.336

ZTFLH:

TG172

Fund: National Key Research and Development Program of China(2022YFC3004502)

Corresponding Authors: CHEN Mindong, E-mail: chenmd.qday@sinopec.com

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	SHEN Zhiyuan
	SHAN Guangbin
	CHEN Mindong
	LIU Yuanshuang

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https://www.jcscp.org/EN/10.11902/1005.4537.2024.336 OR https://www.jcscp.org/EN/Y2025/V45/I5/1433

Fig.1 Comparisons of potential and current signals at different stages of corrosion: (a) passive state, (b) incipient pitting, (c) stable pitting

Fig.2 Comparison of SEM surface morphologies of 304 stainless steel before and after electrochemical noise testing in 3.5%NaCl solution (a) as-received sample; (b) post-test sample

Fig.3 Comparisons of various signal smoothing methods

Fig.4 Comparison of electrochemical noise signals before and after max pooling

Fig.5 Network architecture integrating signal-smoothing spatial pyramid pooling (SPPSS) and residual spatial pyramid pooling (RSPP)

Fig.6 Schematic diagram of SPPSS module calculation process

Fig.7 Schematic diagram of RSPP module calculation process

Fig.8 Loss curves during training and test process

Fig.9 ROC performance of SPPSS-RSPP architecture with 5-fold CV

Table 1 Accuracies of the networks under different random seeds

Fig.10 Comparisons of F1 and accuracy for the networks

Fig.11 Confusion matrices for classification results of different neural networks: (a) baseline, (b) baseline + SPPSS, (c) baseline + SPPSS + RSPP

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