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Condition Monitoring in Manufacturing with Advanced Sensors
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Condition Monitoring in Manufacturing with Advanced Sensors

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 2307

Special Issue Editor

Dr. Uros Zuperl

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: control; monitoring systems; cognitive systems; cyber-physical systems; machining; optimization; modeling; applied artificial intelligence; fixtures in machining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of advanced sensors in condition monitoring has become a transformative factor in modern manufacturing environments. This Special Issue, "Condition Monitoring in Manufacturing with Advanced Sensors", focuses on the integration of sensor technologies to improve process efficiency, product quality, and operational reliability. Condition monitoring systems, powered by a variety of sensors, such as temperature, vibration, and strain sensors, enable the real-time detection of equipment malfunctions and process deviations. This proactive approach minimizes downtime, reduces maintenance costs, and supports predictive maintenance strategies.

This Special Issue is directly aligned with the core scope of Sensors, as it emphasizes the critical role of sensors in industrial applications. The journal promotes advancements in sensor technology, deployment, and integration with intelligent systems, all of which are essential for modern manufacturing. By addressing the development and application of advanced sensors for condition monitoring in manufacturing, this Special Issue contributes significantly to the ongoing discourse on enhancing industrial processes through sensor-driven innovation.

The Special Issue invites contributions that explore innovations in sensor technologies, data analytics, and machine learning for the manufacturing industry. We welcome interdisciplinary research that showcases how advanced sensors can enhance performance, optimize resource usage, and ensure the seamless operation of manufacturing processes.

Dr. Uros Zuperl
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • advanced sensors
  • condition monitoring
  • predictive maintenance
  • smart manufacturing
  • data analytics in manufacturing

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Published Papers (3 papers)

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Research

36 pages, 5791 KiB  
Article
Assessment of Corrosion in Naval Steels Submerged in Artificial Seawater Utilizing a Magnetic Non-Destructive Sensor
by Polyxeni Vourna, Aphrodite Ktena, Evangelos V. Hristoforou and Nikolaos D. Papadopoulos
Sensors 2025, 25(16), 5015; https://doi.org/10.3390/s25165015 - 13 Aug 2025
Viewed by 103
Abstract
This work presents a comprehensive evaluation of corrosion progression in DH36 naval steel through the integration of electrochemical impedance spectroscopy (EIS), weight loss, scanning electron microscopy (SEM), and advanced magnetic non-destructive techniques under artificial seawater (ASW, ASTM D1141) and natural marine conditions. Quantitative [...] Read more.
This work presents a comprehensive evaluation of corrosion progression in DH36 naval steel through the integration of electrochemical impedance spectroscopy (EIS), weight loss, scanning electron microscopy (SEM), and advanced magnetic non-destructive techniques under artificial seawater (ASW, ASTM D1141) and natural marine conditions. Quantitative correlations are established between corrosion layer growth, electrochemical parameters, and magnetic permeability, demonstrating the magnetic sensor’s capacity for the real-time, non-invasive assessment of marine steel degradation. Laboratory exposures reveal a rapid initial corrosion phase with the formation of lepidocrocite and goethite, followed by the densification of the corrosion product layer and a pronounced decline in corrosion rate, ultimately governed by diffusion-controlled kinetics. Notably, changes in magnetic permeability closely track both the thickening of non-magnetic corrosion products and microstructural deterioration, with declining μmax and increased hysteresis widths (FWHM) sensitively indicating evolving surface conditions. A direct comparison with in situ marine immersion at Rafina confirms that the evolution of corrosion morphology and the corresponding magnetic response are further modulated by biofilm development, which exacerbates the attenuation of measured surface permeability and introduces greater variability linked to biological activity. These findings underscore the robustness and diagnostic potential of magnetic non-destructive sensors for the predictive, condition-based monitoring of naval steels, bridging laboratory-controlled observations and complex real-world environments with high quantitative fidelity to corrosion kinetics, phase evolution, and microstructural transformations, thus guiding the strategic deployment of protection and maintenance regimens for naval fleet integrity. Full article
(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)
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19 pages, 2641 KiB  
Article
MSFF-Net: Multi-Sensor Frequency-Domain Feature Fusion Network with Lightweight 1D CNN for Bearing Fault Diagnosis
by Miao Dai, Hangyeol Jo, Moonsuk Kim and Sang-Woo Ban
Sensors 2025, 25(14), 4348; https://doi.org/10.3390/s25144348 - 11 Jul 2025
Viewed by 524
Abstract
This study proposes MSFF-Net, a lightweight deep learning framework for bearing fault diagnosis based on frequency-domain multi-sensor fusion. The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral [...] Read more.
This study proposes MSFF-Net, a lightweight deep learning framework for bearing fault diagnosis based on frequency-domain multi-sensor fusion. The vibration and acoustic signals are initially converted into the frequency domain using the fast Fourier transform (FFT), enabling the extraction of temporally invariant spectral features. These features are processed by a compact one-dimensional convolutional neural network, where modality-specific representations are fused at the feature level to capture complementary fault-related information. The proposed method demonstrates robust and superior performance under both full and scarce data conditions, as verified through experiments on a publicly available dataset. Experimental results on a publicly available dataset indicate that the proposed model attains an average accuracy of 99.73%, outperforming state-of-the-art (SOTA) methods in both accuracy and stability. With only about 70.3% of the parameters of the SOTA model, it offers faster inference and reduced computational cost. Ablation studies confirm that multi-sensor fusion improves all classification metrics over single-sensor setups. Under few-shot conditions with 20 samples per class, the model retains 94.69% accuracy, highlighting its strong generalization in data-limited scenarios. The results validate the effectiveness, computational efficiency, and practical applicability of the model for deployment in data-constrained industrial environments. Full article
(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)
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22 pages, 9488 KiB  
Article
Experimental Study on Drilling Signal Characteristics of PDC Drill Bit in Media of Different Strengths and Identification of Weak Media
by Zheng Wu, Yingbo Fan and Huazhou Chen
Sensors 2024, 24(23), 7852; https://doi.org/10.3390/s24237852 - 8 Dec 2024
Viewed by 1179
Abstract
This study aimed to investigate the drilling signal characteristics when a PDC drill bit penetrates media of different strengths and to assess the potential of these signals for identifying weak layers within rock formations. Laboratory-scale experiments were conducted, and the response characteristics of [...] Read more.
This study aimed to investigate the drilling signal characteristics when a PDC drill bit penetrates media of different strengths and to assess the potential of these signals for identifying weak layers within rock formations. Laboratory-scale experiments were conducted, and the response characteristics of the PDC drill bit in different-strength media were analyzed across the time domain, frequency domain, and time–frequency domain using statistical analysis, Fourier transform, and empirical mode decomposition (EMD). The results indicate that in the lowest-strength concrete (C10), the drilling speed was the fastest, while the mean, median, and primary distribution ranges of the thrust and torque were the smallest. Some dimensionless time-domain and frequency-domain indicators were found to have limitations in differentiating media of varying strengths. Meanwhile, the time–frequency analysis and EMD of the thrust and torque signals revealed distinct changes at the media boundaries, serving as auxiliary criteria for identifying transitions between different media. The time–frequency analysis and EMD demonstrated clear advantages in identifying these boundaries. These findings provide a theoretical basis for using drilling signals to identify weak layers that pose potential roof collapse hazards in roadway roof strata. Full article
(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)
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