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Condition Monitoring in Manufacturing with Advanced Sensors

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 7485

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Special Issue Editor

Dr. Uros Zuperl

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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
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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

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Keywords

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

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Published Papers (5 papers)

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Research

13 pages, 3432 KB

Open AccessArticle

Spindle-Integrated Three-Axis Cutting Force Measurement System for Ultra-Precision Diamond Milling

by Zhongwei Li, Liang An, Yuqi Ding, Huanbin Lin and Yuan-Liu Chen

Sensors 2026, 26(6), 1817; https://doi.org/10.3390/s26061817 - 13 Mar 2026

Viewed by 47

Abstract

Ultra-precision diamond milling is a crucial technology for machining precision components with complex-shaped surfaces and microstructure array surfaces. Machining process monitoring is a promising approach to improving machining quality. This paper proposes a spindle-integrated three-axis cutting force measurement method for ultra-precision diamond milling using force piezoelectric force sensors. A spindle-integrated force measurement mechanism utilizing four piezoelectric force sensors arranged symmetrically and diagonally for measuring three-axis cutting forces was designed. Calibration tests showed that the linearity of force detection in three directions was less than 2%. Tool-setting experiments based on force detection signals were conducted, demonstrating the capacity of precision tool-setting in the Z-direction with an accuracy of less than 100 nm. A Wiener filter was employed to eliminate measurement noise from vibration and inertial forces under spindle rotation. Ultra-precision milling experiments were carried out based on the designed spindle-integrated force measurement mechanism, and the measurement results demonstrated that the system could effectively detect cutting forces below 50 mN and exhibited good correlation with the measurement results of commercial standard dynamometers. This paper provides a promising and effective in-process force measurement technology for the ultra-precision milling process. Full article

(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)

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19 pages, 1457 KB

Open AccessArticle

Practical Test-Time Domain Adaptation for Industrial Condition Monitoring by Leveraging Normal-Class Data

by Payman Goodarzi and Andreas Schütze

Sensors 2025, 25(24), 7614; https://doi.org/10.3390/s25247614 - 15 Dec 2025

Cited by 1 | Viewed by 843

Abstract

Machine learning has driven significant advancements across diverse domains. However, models often experience performance degradation when applied to data distributions that differ from those encountered during training, a challenge known as domain shift. This issue is particularly relevant in industrial condition monitoring, where data originate from heterogeneous sensors operating under varying conditions, hardware configurations, or environments. Domain adaptation is a well-known method to address this problem; however, the proposed methods are not directly applicable in real-world condition monitoring scenarios. This study addresses such challenges by introducing a Normal-Class Test-Time Domain Adaptation (NC-TTDA) framework tailored for condition monitoring applications. The proposed framework detects distributional shifts in sensor data and adapts pretrained models to new operating conditions by exploiting readily available normal-class samples, without requiring labeled target data. Furthermore, it integrates seamlessly with automated machine learning (AutoML) workflows to support hyperparameter optimization, model selection, and test-time adaptation within an end-to-end pipeline. Experiments conducted on six publicly available condition monitoring datasets demonstrate that the proposed approach achieves robust generalization under domain shift, yielding average AUROC scores above 99% and low false positive rates across all target domains. This work emphasizes the need for practical solutions to address domain adaptation in condition monitoring and highlights the effectiveness of NC-TTDA for real-world industrial monitoring applications. Full article

(This article belongs to the Special Issue Condition Monitoring in Manufacturing with Advanced Sensors)

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36 pages, 5791 KB

Open AccessArticle

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

Cited by 3 | Viewed by 1692

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 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 KB

Open AccessArticle

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

Cited by 7 | Viewed by 2406

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 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 KB

Open AccessArticle

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 1726

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 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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