Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Advanced Sensor Technologies for Corrosion Monitoring
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensor Technologies for Corrosion Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: 25 October 2026 | Viewed by 7338

Special Issue Editors

Dr. Jiantao Qi

E-Mail Website
Guest Editor
College of New Energy, China University of Petroleum (East China), Qingdao 266580, China
Interests: light alloy corrosion and surface treatment; electrochemical evaluation of anti-corrosion coating effects; surface interface physics and chemistry; surface-enhanced Raman spectroscopy analysis; Raman–electrochemical in situ testing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaolong Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Shandong University, Jinan 250061, China
Interests: equipment sensing and monitoring technology; electrochemical energy storage materials; membrane science; the diagnosis of high voltage equipment insulation

Special Issue Information

Dear Colleagues,

Corrosion is the main reason for the performance degradation of metal components in natural environments; this can affect the durability of component structures and shorten their service life. Severe cases can lead to component failure and cause safety accidents. Early detection and diagnosis alongside effective prevention and control measures are important components of corrosion protection for metal components. Corrosion monitoring technology can provide a basis from which to evaluate the rationality of material selection, the effectiveness of protective measures, and environmental conditions.

Corrosion monitoring technology can provide corrosion information at any time without manual testing, ensuring the consistency and reliability of test results. Installing sensors and monitoring instruments on key structures to monitor the entire process of corrosion in key parts/structures of equipment is beneficial for the real-time prevention and control of corrosion, and can also provide more data support for equipment life extension.

Dr. Jiantao Qi
Prof. Dr. Xiaolong Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • corrosion
  • monitor
  • life assessment
  • sensors

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 2641 KB  
Article
A Small-Sample Fault Diagnosis Method for High-Voltage Circuit Breaker Spring Mechanisms Based on Multi-Source Feature Fusion and Stacking Ensemble Learning
by Xining Li, Hanyan Xiao, Ke Zhao, Lei Sun, Tianxin Zhuang, Haoyan Zhang and Hongwei Mei
Sensors 2026, 26(5), 1485; https://doi.org/10.3390/s26051485 - 26 Feb 2026
Cited by 1 | Viewed by 1275
Abstract
To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking [...] Read more.
To address the practical engineering challenges of limited fault samples for high-voltage circuit breaker spring operating mechanisms and the inability of single features to fully reflect equipment status, this paper proposes a small-sample fault diagnosis method based on multi-source feature fusion and Stacking ensemble learning. First, a multi-source sensing system containing MEMS (Micro-Electro-Mechanical System) pressure and travel, coil, and motor current was constructed to achieve comprehensive monitoring of the mechanical and electrical states of a 220 kV circuit breaker; in particular, the introduction of non-invasive MEMS sensors effectively solves the difficulty of capturing static spring fatigue characteristics inherent in traditional methods. Second, a high-dimensional feature space was constructed using Savitzky–Golay filtering and physical feature extraction techniques. To address the characteristics of small-sample data distribution, a two-layer Stacking ensemble learning model based on 5-fold cross-validation was designed. This model utilizes the SVM (Support Vector Machine), RF (Random Forest), and KNN (K-Nearest Neighbors) as base classifiers and Logistic Regression as the meta-learner, achieving an adaptive fusion of the advantages of heterogeneous algorithms. True-type experimental results show that the average diagnostic accuracy of this method under normal conditions and four typical fault conditions reaches 96.1%, which is superior to single base models (the RF was 94.2%). Feature importance analysis further confirms that closing and opening pressures are the most critical features for distinguishing mechanical faults. This study provides effective theoretical basis and technical support for condition-based maintenance of high-voltage circuit breakers under small-sample conditions. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)
►▼ Show Figures

Figure 1

18 pages, 1123 KB  
Article
Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models
by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta and José Valeriano Alvarez-Cabal
Sensors 2025, 25(13), 4231; https://doi.org/10.3390/s25134231 - 7 Jul 2025
Cited by 3 | Viewed by 2392
Abstract
Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there [...] Read more.
Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 2531 KB  
Review
Recent Advances in Raman Spectral Classification with Machine Learning
by Yonghao Liu, Yizhan Wu, Junjie Wang, Jiantao Qi, Changjing Zhou and Yuhua Xue
Sensors 2026, 26(1), 341; https://doi.org/10.3390/s26010341 - 5 Jan 2026
Cited by 9 | Viewed by 2914
Abstract
Raman spectroscopy is a non-destructive analytical technique based on molecular vibrational properties. However, its practical application is often challenged by weak scattering signals, complex spectra, and the high-dimensional nature of the data, which complicates accurate interpretation. Traditional chemometric methods are limited in handling [...] Read more.
Raman spectroscopy is a non-destructive analytical technique based on molecular vibrational properties. However, its practical application is often challenged by weak scattering signals, complex spectra, and the high-dimensional nature of the data, which complicates accurate interpretation. Traditional chemometric methods are limited in handling complex, nonlinear Raman data and rely on tedious, expert-knowledge-based feature engineering. The fusion of data-driven Machine Learning (ML) and Deep Learning (DL) methods offers a robust solution, enabling the automatic learning of complex features from raw data and achieving high-accuracy classification and prediction. The present study employed a structured narrative review methodology to capture the research progress, current trends, and future directions in the field of ML-assisted Raman spectral classification. This review provides a comprehensive overview of the application of traditional ML models and advanced DL architectures in Raman spectral analysis. It highlights the latest applications of this technology across several key domains, including biomedical diagnostics, food safety and authentication, mineralogical classification, and plastic and microplastic identification. Despite recent progress, several challenges remain: limited training data, weak cross-dataset generalization, poor reproducibility, and limited interpretability of deep models. We also outline practical directions for future research. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop