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Machines
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Intelligent Vibration Control and Condition Monitoring in Smart Structures and...
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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: 31 May 2026 | Viewed by 2833

Special Issue Editors

Dr. Haitao Li

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Guest Editor
National Laboratory for Rail Transit, Southwest Jiaotong University, Chengdu 610031, China
Interests: vibration control of electromechanical systems
Dr. Wen Ji

E-Mail
Guest Editor
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

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. Machines is an international peer-reviewed open access monthly 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 2400 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

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

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

Published Papers (3 papers)

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Research

30 pages, 852 KB  
Article
Bayesian Model Updating of Structural Parameters Using Temperature Variation Data: Simulation
by Ujjwal Adhikari and Young Hoon Kim
Machines 2025, 13(10), 899; https://doi.org/10.3390/machines13100899 - 1 Oct 2025
Viewed by 461
Abstract
Finite element (FE) models are widely used in structural health monitoring to represent real structures and assess their condition, but discrepancies often arise between numerical and actual structural behavior due to simplifying assumptions, uncertain parameters, and environmental influences. Temperature variation, in particular, significantly [...] Read more.
Finite element (FE) models are widely used in structural health monitoring to represent real structures and assess their condition, but discrepancies often arise between numerical and actual structural behavior due to simplifying assumptions, uncertain parameters, and environmental influences. Temperature variation, in particular, significantly affects structural stiffness and modal properties, yet it is often treated as noise in traditional model updating methods. This study treats temperature changes as valuable information for model updating and structural damage quantification. The Bayesian model updating approach (BMUA) is a probabilistic approach that updates uncertain model parameters by combining prior knowledge with measured data to estimate their posterior probability distributions. However, traditional BMUA methods assume mass is known and only update stiffness. A novel BMUA framework is proposed that incorporates thermal buckling and temperature-dependent stiffness estimation and introduces an algorithm to eliminate the coupling effect between mass and stiffness by using temperature-induced stiffness changes. This enables the simultaneous updating of both parameters. The framework is validated through numerical simulations on a three-story aluminum shear frame under uniform and non-uniform temperature distributions. Under healthy and uniform temperature conditions, stiffness parameters were estimated with high accuracy, with errors below 0.5% and within uncertainty bounds, while mass parameters exhibited errors up to 13.8% that exceeded their extremely low standard deviations, indicating potential model bias. Under non-uniform temperature distributions, accuracy declined, particularly for localized damage cases, with significant deviations in both parameters. Full article
(This article belongs to the Special Issue Intelligent Vibration Control and Condition Monitoring in Smart Structures and Electromechanical Systems)
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20 pages, 5624 KB  
Article
Active Control Method for Pantograph-Catenary System Based on Neural Network PID Under Crosswind Conditions
by Mengyao Wang, Yan Xu, Like Pan, William Zhendong Liu and Ziwei Zhou
Machines 2025, 13(10), 897; https://doi.org/10.3390/machines13100897 - 1 Oct 2025
Viewed by 298
Abstract
Crosswind is a critical environmental factor affecting the dynamic interaction between the pantograph and catenary in high-speed trains, which can severely compromise the operational stability of the system. To address this challenge, this study develops an active pantograph control scheme for crosswind disturbances [...] Read more.
Crosswind is a critical environmental factor affecting the dynamic interaction between the pantograph and catenary in high-speed trains, which can severely compromise the operational stability of the system. To address this challenge, this study develops an active pantograph control scheme for crosswind disturbances by employing a neural network-based PID controller. First, the target value is determined based on the train operating speed and inherent data of the pantograph-catenary system, and a PID controller is constructed. Subsequently, a neural network is integrated into the controller to train the system’s output contact force and PID parameters using its nonlinear approximation capability, thereby optimizing the parameters and achieving effective control of the system. The effectiveness of the controller is then validated by applying the proposed method to a high-speed train pantograph-catenary system under crosswind conditions, with its control performance thoroughly analyzed. The results indicate that the proposed control scheme demonstrates effective regulation of the pantograph-catenary system across various typical crosswind scenarios, achieving significant reduction or even complete elimination of pantograph-catenary’s contact loss rate while exhibiting strong robustness, thereby proving fully applicable for practical implementation in high-speed railway engineering applications. Full article
(This article belongs to the Special Issue Intelligent Vibration Control and Condition Monitoring in Smart Structures and Electromechanical Systems)
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22 pages, 3848 KB  
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
Cited by 1 | Viewed by 1632
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
(This article belongs to the Special Issue Intelligent Vibration Control and Condition Monitoring in Smart Structures and Electromechanical Systems)
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