Intelligent Vibration Control and Condition Monitoring in Smart Structures and Electromechanical Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1038

Special Issue Editors

National Laboratory for Rail Transit, Southwest Jiaotong University, Chengdu 610031, China
Interests: vibration control of electromechanical systems
National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai, China
Interests: high-speed maglev transport

Special Issue Information

Dear Colleagues,

The integration of intelligent vibration control and condition monitoring in smart structures and electromechanical systems has become a crucial research area, driven by advancements in sensing technologies, artificial intelligence, and adaptive control strategies. This Special Issue aims to present cutting-edge research on vibration suppression, structural health monitoring, predictive maintenance, and dynamic performance optimization in modern engineering systems.

Key topics of interest include, but are not limited to, the following:

  • Smart structures with adaptive and self-regulating vibration control capabilities;
  • Active, semi-active, and passive vibration control in electromechanical systems;
  • Electromechanical coupling effects and their impact on system dynamics;
  • Innovative sensing and actuation technologies for vibration reduction.

This Special Issue aims to foster interdisciplinary collaboration and bridge the gap between theory and real-world applications in transportation systems, intelligent manufacturing, robotics, energy systems, and smart infrastructure. We welcome researchers and practitioners to contribute original research, review articles, and case studies that advance the state of the art in this dynamic field.

Dr. Haitao Li
Dr. Wen Ji
Guest Editors

Manuscript Submission Information

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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. Machines is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • intelligent vibration control
  • condition monitoring
  • smart structures
  • electromechanical systems

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Published Papers (1 paper)

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Research

22 pages, 3848 KiB  
Article
A Multi-Category Defect Detection Model for Rail Fastener Based on Optimized YOLOv8n
by Mei Chen, Maolin Zhang, Jun Peng, Jiabin Huang and Haitao Li
Machines 2025, 13(6), 511; https://doi.org/10.3390/machines13060511 - 12 Jun 2025
Viewed by 829
Abstract
Currently, object detection-based rail fastener defect detection methods still face challenges such as limited detection categories, insufficient accuracy, and high computational complexity. To this end, the YOLOv8n-FDD, an advanced multi-category fastener defect detection model designed upon the YOLOv8n with comprehensive optimizations is developed [...] Read more.
Currently, object detection-based rail fastener defect detection methods still face challenges such as limited detection categories, insufficient accuracy, and high computational complexity. To this end, the YOLOv8n-FDD, an advanced multi-category fastener defect detection model designed upon the YOLOv8n with comprehensive optimizations is developed in this paper. Concretely, by introducing the CUT-based style transfer model to generate diverse defect samples, the concern due to imbalanced distribution of sample categories is effectively alleviated. The CA mechanism is incorporated to enhance the feature extraction capability, and the bounding box loss function is further upgraded to improve the model’s generalization performance. With respect to efficiency, the Conv and c2f modules of the YOLOv8n model are, respectively, replaced with the GSConv and VoVGSPCP modules, accordingly achieving a lightweight design. Comparative experimental results demonstrate that the presented YOLOv8n-FDD model outperforms several classic object detection models in terms of detection accuracy, detection speed, model size, and computational complexity. Full article
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