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Advanced Sensing and Measurement for Multi-Domain Interactions in Electrified Transportation Systems

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

Deadline for manuscript submissions: 25 November 2026 | Viewed by 269

Editors


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Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: advanced intelligent rail current collection system; advanced material for electro-mechanical contact; current collection performance of high-speed train; high-speed railway pantograph–catenary interaction

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Guest Editor
School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: vehicle dynamics; mechatronics; control engineering; vibration analysis; autonomous vehicle; multibody simulation
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Guest Editor
Department of Engineering Structures, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628CN Delft, The Netherlands
Interests: sensors; signal processing; machine learning; digital twins and their applications in the health monitoring and asset management of railway infrastructures and vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrified transportation systems are rapidly evolving toward higher speed, higher efficiency, greater intelligence, and deeper system integration. In these systems, many critical engineering challenges are inherently governed by multi-domain interactions, rather than by a single physical process alone. Electrical, mechanical, thermal, magnetic, tribological, and control-related effects are often strongly coupled; such couplings directly affect system reliability, energy efficiency, safety, and service life. Therefore, advanced sensing and measurement techniques are becoming increasingly important for revealing coupled behaviors, quantifying system states, and supporting the diagnosis and mitigation of complex engineering problems.

Representative examples can be found across a wide range of applications. In high-speed railways, electric sliding contacts such as pantograph–catenary systems involve coupled current transfer, contact mechanics, vibration, wear, temperature rise, and arcing. In traction motors and drive systems, electromagnetic, thermal, structural, and control effects interact continuously during operation. Similar coupled phenomena also arise in electrified vehicles, photovoltaic-assisted transportation platforms, and aerospace electromechanical interfaces such as satellites. In many cases, understanding and solving these engineering problems fundamentally rely on the accurate sensing, measurement, monitoring, and interpretation of multi-domain behaviors.

This Special Issue aims to present and disseminate the latest advances in sensing, measurement, monitoring, modelling, and diagnostics for multi-domain interactions in electrified transportation systems. We welcome contributions addressing novel sensors, experimental methods, signal processing, data-driven approaches, digital twins, intelligent condition monitoring, fault diagnosis, and performance evaluation for coupled engineering systems. Research that improves the understanding, characterization, and management of multi-domain interactions in practical transportation applications is particularly encouraged.

Topics of interest for publication include, but are not limited to, the following:

  • The sensing and measurement of multi-domain interactions in electrified transportation systems.
  • The multi-physics modelling and experimental characterization of coupled engineering phenomena.
  • Electric sliding contact monitoring and diagnostics.
  • Sensing and diagnostics for traction motors and drive systems.
  • Thermal, electromagnetic, and mechanical interaction analysis in transport equipment.
  • Intelligent condition monitoring and fault diagnosis for coupled systems.
  • Signal processing and feature extraction for multi-domain measurements.
  • Digital twins and data-driven methods for coupled transportation systems.
  • Sensor fusion for complex state perception and health assessment.
  • Reliability, durability, and safety evaluation under multi-domain coupling conditions.

Prof. Dr. Yang Song
Dr. Saikat Dutta
Dr. Hongrui Wang
Guest Editors

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Keywords

  • multi-domain interactions
  • sensing and measurement
  • electrified transportation systems
  • multi-physics coupling
  • electric contact
  • traction motors
  • condition monitoring
  • fault diagnosis
  • digital twin
  • sensor fusion
  • electro-mechanical–thermal coupling
  • mechanical–electro coupling in photovoltaics
  • electro-induced wear

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Published Papers (1 paper)

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Research

23 pages, 45769 KB  
Article
FF-DEIM: DEIM with Image Dehazing and Self-Supervised Pretraining for Catenary Support Component Detection
by Lingzhi Zhang, Jinyong Huang, Guojin Qin, Jincheng Cao, Fei Fan, Hui Wang and Haonan Yang
Sensors 2026, 26(15), 5000; https://doi.org/10.3390/s26155000 - 6 Aug 2026
Abstract
The catenary support component (CSC) is a key part of the electrified railway system, and its operational status directly affects railway operational safety. These components’ images are collected using inspection equipment and detected using computer vision techniques. However, catenary network inspection faces the [...] Read more.
The catenary support component (CSC) is a key part of the electrified railway system, and its operational status directly affects railway operational safety. These components’ images are collected using inspection equipment and detected using computer vision techniques. However, catenary network inspection faces the following issues: (1) due to limitations in the equipment’s shooting angle and changes in viewing distance, the collected images contain multi-scale and multi-class problems, and (2) the railway environment is highly variable, and adverse weather conditions such as fog, rain, and low light affect the imaging devices, leading to degraded image quality. To address these issues, this paper proposes a novel detection framework, FF-DEIM, for detecting catenary support components. First, a dual-channel fusion network (DCFNet) is introduced, which significantly improves image quality by removing foreground interferences such as fog, raindrops, and dynamic blur. Second, a pretraining framework based on contrastive learning, mask image modeling with contrastive learning (MIMCL), is designed to enhance the model’s focus on key regions of the catenary network components, optimizing feature extraction capabilities and improving model convergence speed. Then, a feature-focusing pyramid network (FFPN) is proposed, which uses the focus feature module to fuse cross-level contextual features, enhancing the ability to capture local details and improving the model’s small object detection performance. Finally, a drone-based catenary network image dataset, including scenes with fog, rain, and low light, is constructed, and experiments validate the effectiveness of the proposed method. Full article
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