Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery
share announcement

Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 2860

Special Issue Editors

Dr. Qingyu Zhu

E-Mail
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: mechanical dynamics and vibration analysis; advanced sensing testing and diagnostic methods; structural vibration and noise control; vibration energy harvesting
Dr. Xiantao Zhang

E-Mail
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: triboelectric nanogenerators; rotor dynamics and fault diagnosis
Prof. Dr. Zhaoye Qin

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing, China
Interests: nanocomposites; vibration of plates and shells; vibration control; rotordynamics; health monitoring; vibration energy harvesting

Special Issue Information

Dear Colleagues,

Rotating machinery serves as a core component of modern industrial systems, with wide applications in aerospace, energy, transportation, etc. Its operational conditions are closely tied to equipment reliability. The vibration signals generated during operation contain rich information about machine states, making the study of vibration characteristics, testing, and monitoring technologies a long-standing research focus. In recent years, rapid advancements in micro/nano-fabrication, wireless communication, and artificial intelligence have driven significant progress in vibration testing and intelligent monitoring of rotating machinery. Notably, innovations in embedded device design, wireless sensing systems, vibration energy harvesting and self-powered monitoring, and deep learning-based fault diagnosis models have shown great potential. These technologies enable the precise sensing of components in complex systems and support the shift from wired, centralized monitoring to low-power, autonomous, and distributed wireless sensing networks. This Special Issue aims to gather the latest research and practical advances in this field, focusing on (but not limited to) the following topics:

  • Dynamic modeling and vibration analysis of rotating machinery;
  • Vibration testing and in situ measurement methods;
  • Embedded monitoring systems;
  • Self-powered sensor design;
  • Deployment and robustness of wireless sensor networks;
  • Intelligent monitoring of critical components in aero-engines;
  • Fault mechanism analysis and diagnostic techniques;
  • AI-enabled condition monitoring.

Dr. Qingyu Zhu
Dr. Xiantao Zhang
Prof. Dr. Zhaoye Qin
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 250 words) can be sent to the Editorial Office for assessment.

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

  • rotating machinery
  • rotor dynamics
  • vibration energy harvesting
  • self-powered sensors
  • intelligent monitoring

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 5390 KB  
Article
Multilevel Modeling and Validation of Thermo-Mechanical Nonlinear Dynamics in Flexible Supports
by Xiangyu Meng, Qingyu Zhu, Qingkai Han and Junzhe Lin
Machines 2026, 14(1), 131; https://doi.org/10.3390/machines14010131 - 22 Jan 2026
Viewed by 370
Abstract
Prediction accuracy for complex flexible support systems is often limited by insufficiently characterized thermo-mechanical couplings and nonlinearities. To address this, we propose a multilevel hybrid parallel–serial model that integrates the thermo-viscous effects of a Squeeze Film Damper (SFD) via a coupled Reynolds–Walther equation, [...] Read more.
Prediction accuracy for complex flexible support systems is often limited by insufficiently characterized thermo-mechanical couplings and nonlinearities. To address this, we propose a multilevel hybrid parallel–serial model that integrates the thermo-viscous effects of a Squeeze Film Damper (SFD) via a coupled Reynolds–Walther equation, the structural flexibility of a squirrel-cage support using Finite Element analysis, and the load-dependent stiffness of a four-point contact ball bearing based on Hertzian theory. The resulting state-dependent system is solved using a force-controlled iterative numerical algorithm. For validation, a dedicated bidirectional excitation test rig was constructed to decouple and characterize the support’s dynamics via frequency-domain impedance identification. Experimental results indicate that equivalent damping is temperature-sensitive, decreasing by approximately 50% as the lubricant temperature rises from 30 °C to 100 °C. In contrast, the system exhibits pronounced stiffness hardening under increasing loads. Theoretical analysis attributes this nonlinearity primarily to the bearing’s Hertzian contact mechanics, which accounts for a stiffness increase of nearly 240%. This coupled model offers a distinct advancement over traditional linear approaches, providing a validated framework for the design and vibration control of aero-engine flexible supports. Full article
(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)
►▼ Show Figures

Figure 1

25 pages, 5517 KB  
Article
A Novel Online Real-Time Prediction Method for Copper Particle Content in the Oil of Mining Equipment Based on Neural Networks
by Long Yuan, Zibin Du, Xun Gao, Yukang Zhang, Liusong Yang, Yuehui Wang and Junzhe Lin
Machines 2026, 14(1), 76; https://doi.org/10.3390/machines14010076 - 8 Jan 2026
Cited by 1 | Viewed by 398
Abstract
For the problem of online real-time prediction of copper particle content in the lubricating oil of the main spindle-bearing system of mining equipment, the traditional direct detection method is costly and has insufficient real-time performance. To this end, this paper proposes an indirect [...] Read more.
For the problem of online real-time prediction of copper particle content in the lubricating oil of the main spindle-bearing system of mining equipment, the traditional direct detection method is costly and has insufficient real-time performance. To this end, this paper proposes an indirect prediction method based on data-driven neural networks. The proposal of this method is based on a core assumption: during the stable wear stage of the equipment, there exists a modelable statistical correlation between the copper particle content in the oil and the total amount of non-ferromagnetic particles that are easy to measure online. Based on this, a neural network prediction model was constructed, with the online metal abrasive particle sensor signal (non-ferromagnetic particle content) as the input and the copper particle content as the output. The experimental data are derived from 100 real oil samples collected on-site from the lubrication system of the main shaft bearing of a certain mine mill. To enhance the model’s performance in the case of small samples, data augmentation techniques were adopted in the study. The verification results show that the average prediction accuracy of the proposed neural network model reaches 95.66%, the coefficient of determination (R2) is 0.91, and the average absolute error (MAE) is 0.3398. Its performance is significantly superior to that of the linear regression model used as the benchmark (with an average accuracy of approximately 80%, R2 = 0.71, and the mean absolute error (MAE) = 1.5628). This comparison result not only preliminarily verified the validity of the relevant hypotheses of non-ferromagnetic particles and copper particles in specific scenarios, but also revealed the nonlinear nature of the relationship between them. This research explores and preliminarily validates a low-cost technical path for the online prediction of copper particle content in the stable wear stage of the main shaft bearing system, suggesting its potential for engineering application within specific, well-defined scenarios. Full article
(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)
►▼ Show Figures

Figure 1

15 pages, 5285 KB  
Article
A Multi-Layer Triboelectric Material Deep Groove Ball Bearing Triboelectric Nanogenerator: Speed and Skidding Monitoring
by Zibao Zhou, Long Wang, Zihao Wang and Fengtao Wang
Machines 2025, 13(9), 875; https://doi.org/10.3390/machines13090875 - 19 Sep 2025
Viewed by 1296
Abstract
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip [...] Read more.
With the ongoing advancement of triboelectric nanogenerator (TENG) technology, a novel internal integrated monitoring sensor has been introduced for traditional industrial equipment. A multilayer triboelectric material deep groove ball triboelectric nanogenerator (DGTG) device has been proposed to monitor the rotational speed and slip state of the rolling elements. The DGTG utilizes a copper inner ring charge supplementation mechanism to maintain the maximum charge density on the rolling element, thereby ensuring a strong electrical signal output. The deviation between the output frequency of the electrical signal and the theoretical value allows for effective monitoring of the slip state during bearing operation. Experimental results demonstrate that when the inner ring speed ranges from 100 to 2000 rpm, the open-circuit voltage generally remains above 30 V. The short-circuit current signal exhibits a fitting coefficient of R2 = 0.99997 with respect to the roller’s rotational speed frequency and motor speed, while the open-circuit voltage signal shows a fitting coefficient of R2 = 0.99984, indicating a strong linear relationship and a good response to varying speeds. Compared to the traditional photoelectric sensors commonly used in industry, the measurement difference between the three signals is consistently less than 5.5%, and real-time monitoring of the slip rate is possible when compared to the theoretical value. The DGTG developed in this study occupies minimal space, offers high reliability, and fully leverages the bearing structure, enabling real-time monitoring of bearing speed and slip. Full article
(This article belongs to the Special Issue Vibration Energy Harvesting and Intelligent Monitoring for Rotating Machinery)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop