Corrosion type identification in flanged joints using recurrent neural networks on electrochemical noise measurements.

IF 7.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
npj Materials Degradation Pub Date : 2025-01-01 Epub Date: 2025-07-15 DOI:10.1038/s41529-025-00638-y
Soroosh Hakimian, Abdel-Hakim Bouzid, Lucas A Hof
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引用次数: 0

Abstract

Bolted flanged joints are essential for connecting piping and process equipment but are vulnerable to localized corrosion that leads to sudden, unpredictable leaks. Electrochemical noise (EN) measurements can detect such corrosion, yet processing EN data is time-consuming and requires expertise. This study applies recurrent neural networks (RNNs) to automate corrosion type identification on flange surfaces using raw EN signals from spontaneous electrochemical reactions. In this work, supervised, hybrid, and unsupervised ML approaches are evaluated using experimentally obtained EN data. Among supervised models, the long short-term memory (LSTM) model achieves 93.62% accuracy. A hybrid method combining LSTM autoencoder features with a random forest classifier improves accuracy to 97.85%. An unsupervised method using LSTM autoencoder, principal component analysis, and k-means clustering also shows strong potential for real-time corrosion monitoring. Automated identification of corrosion types on flanged joints supports more effective material protection strategies, reducing the risk of failure in critical infrastructure.

基于电化学噪声测量的递归神经网络法兰连接腐蚀类型识别。
螺栓法兰连接对于连接管道和工艺设备至关重要,但容易受到局部腐蚀,导致突然的、不可预测的泄漏。电化学噪声(EN)测量可以检测到这种腐蚀,但处理EN数据既耗时又需要专业知识。本研究应用递归神经网络(rnn),利用自发电化学反应产生的原始EN信号,自动识别法兰表面的腐蚀类型。在这项工作中,使用实验获得的EN数据评估了有监督、混合和无监督的机器学习方法。在监督模型中,长短期记忆(LSTM)模型的准确率达到了93.62%。将LSTM自编码器特征与随机森林分类器相结合的混合方法将准确率提高到97.85%。使用LSTM自编码器、主成分分析和k-means聚类的无监督方法也显示出实时腐蚀监测的强大潜力。自动识别法兰连接上的腐蚀类型支持更有效的材料保护策略,降低关键基础设施的故障风险。
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来源期刊
npj Materials Degradation
npj Materials Degradation MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
7.80
自引率
7.80%
发文量
86
审稿时长
6 weeks
期刊介绍: npj Materials Degradation considers basic and applied research that explores all aspects of the degradation of metallic and non-metallic materials. The journal broadly defines ‘materials degradation’ as a reduction in the ability of a material to perform its task in-service as a result of environmental exposure. The journal covers a broad range of topics including but not limited to: -Degradation of metals, glasses, minerals, polymers, ceramics, cements and composites in natural and engineered environments, as a result of various stimuli -Computational and experimental studies of degradation mechanisms and kinetics -Characterization of degradation by traditional and emerging techniques -New approaches and technologies for enhancing resistance to degradation -Inspection and monitoring techniques for materials in-service, such as sensing technologies
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