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Machines
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Advancements in Mechanical Power Transmission and Its Elements: Second Edition
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Advancements in Mechanical Power Transmission and Its Elements: Second Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1063

Special Issue Editors

Dr. Gang Li

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Mississippi State University, 479-1 Hardy Road, Starkville, MS 39762, USA
Interests: design of gears; marine renewable energy; wind energy; advanced control theory; mechatronics; control; drivetrains; mechanism design; wear; dynamics; vibration
Special Issues, Collections and Topics in MDPI journals
Dr. Yawen Wang

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: gear noise/vibration; structural dynamics; vibro-acoustics; active noise and vibration control, automotive NVH (noise, vibration, and harshness); electro-mechanical system dynamics; data-driven condition monitoring and prognostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the Special Issue, ‘Advancements in Mechanical Power Transmission and Its Elements’ (https://www.mdpi.com/journal/machines/special_issues/3OW18A1VD8), we are pleased to announce the next in the series, entitled ‘Advancements in Mechanical Power Transmission and Its Elements: Second Edition’.

Mechanical power transmission plays a pivotal role in various industries, enabling the efficient transfer of power from a source to a driven load. The continuous advancement of technologies and innovative elements in this field has revolutionized the performance, reliability, and sustainability of power transmission systems.

This Special Issue aims to explore the recent developments and emerging trends in mechanical power transmission, focusing on the advancements related to its elements and the integration of novel technologies. This Special Issue aims to provide a comprehensive platform for researchers and industry professionals to share their knowledge, experiences, and advancements in this critical area.

Potential subtopics for this Special Issue include, but are not limited to, the following:

Next-generation gearing systems:

  • Design optimization of gears for improved efficiency and noise reduction.
  • Advanced materials and manufacturing techniques for high-performance gears.
  • Advanced tooth contact analysis and dynamic analysis of gear systems.
  • Lubrication of gear systems.

Bearings and rolling element technologies:

  • Novel bearing designs for enhanced load capacity and reduced friction losses.
  • Application of advanced materials in bearings for increased durability and reliability.
  • Development of smart bearings with condition monitoring capabilities.

Innovative drivetrain designs:

  • High-strength materials and composite technologies for improved drivetrain reliability performance.
  • New control strategies for drivetrains.
  • Advancements in e-drive systems for electrical vehicles to enhance efficiency.
  • Integration of smart sensors and IoT for real-time monitoring and predictive maintenance.

Cutting-edge couplings and clutches:

  • Flexible coupling designs for misalignment compensation and vibration dampening.
  • Smart clutches and brakes with precise engagement and disengagement capabilities.
  • Electromagnetic couplings and clutches for efficient power transmission control.

Dr. Gang Li
Dr. Yawen Wang
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

  • gears
  • bearings
  • drivetrain
  • clutches
  • dynamic analysis
  • control
  • condition monitoring
  • lubrication

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 6192 KB  
Article
A Data-Driven Fault Diagnosis Method for Marine Steam Turbine Condensate System Based on Deep Transfer Learning
by Yuhui Liu, Liping Chen, Duansen Shangguan and Chengcheng Yu
Machines 2025, 13(8), 708; https://doi.org/10.3390/machines13080708 - 10 Aug 2025
Viewed by 437
Abstract
Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained [...] Read more.
Accurate fault diagnosis in marine steam turbine condensate systems is challenged by insufficient real fault samples and dynamic operational conditions. To address this limitation, DTL-DFD, a novel framework integrating digital twins (DTs) and deep transfer learning (DTL), is proposed, wherein a high-fidelity physics-constrained digital twin model is constructed through the systematic injection of six diagnostic classes (1 normal + 5 faults), including insufficient circulation water flow.Through an innovative all-layer parameter initialization with a partial fine-tuning (ALPT-PF) strategy, all weights and biases from a pre-trained one-dimensional convolutional neural network (1D-CNN) were fully transferred to the target model, which was subsequently fine-tuned via a hierarchical learning rate mechanism to adapt to real-world distribution discrepancies. Experimental results demonstrate 94.34% accuracy on cross-distribution test sets with a 4.72% improvement over state-of-the-art methods, confirming significant enhancements in generalization capability and diagnostic stability under small-sample conditions with significant real data reduction, thereby providing an effective solution for the intelligent operation and maintenance of marine steam turbine systems. Full article
(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements: Second Edition)
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17 pages, 4466 KB  
Article
An Oil Debris Analysis Method of Gearbox Condition Monitoring Based on an Improved Multi-Variable Grey Prediction Model
by Bo Wang and Yizhong Wu
Machines 2025, 13(8), 664; https://doi.org/10.3390/machines13080664 - 29 Jul 2025
Viewed by 453
Abstract
Accurate oil debris analysis and wear monitoring of a gearbox are essential to ensure its stable and reliable operation. Element types of wear debris and their changes in the lubrication oil of the gearbox can be monitored by spectral analysis. However, it is [...] Read more.
Accurate oil debris analysis and wear monitoring of a gearbox are essential to ensure its stable and reliable operation. Element types of wear debris and their changes in the lubrication oil of the gearbox can be monitored by spectral analysis. However, it is still difficult to identify wear parts of the gearbox due to the complex composition of elements of wear debris. An improved multi-variable grey prediction model by incorporating a multi-objective genetic algorithm (MOGA-GM(1, N)) is proposed to evaluate weight coefficients of element concentrations of wear debris in the lubrication oil of the gearbox. Moreover, a wear growth rate of each element in the lubrication oil is proposed as an index for oil debris analysis to analyze the multi-variable correlation between the common element of iron (Fe) and other related elements of wear parts of the gearbox. Oil debris analysis of the gearbox is conducted on optimal weight coefficients of related elements to the common element Fe using the MOGA-GM(1, N) model. Wear experiment results verify feasibility of the proposed oil debris analysis method. Full article
(This article belongs to the Special Issue Advancements in Mechanical Power Transmission and Its Elements: Second Edition)
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