Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Special Issues
New Approaches in Rotating Machinery Modelling, Analysis and Monitoring
share announcement

New Approaches in Rotating Machinery Modelling, Analysis and Monitoring

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 3790

Special Issue Editor

Dr. Helio Fiori De Castro

E-Mail Website
Guest Editor
School of Mechanical Engineering, State University of Campinas, Campinas, Brazil
Interests: rotor dynamics; uncertainties; fault diagnosis

Special Issue Information

Dear Colleagues,

With the increase in modern computing capabilities, new methods are required for the design optimization, stochastic modelling, experiment, condition monitoring, diagnostics, and prognostics of bearings and rotor systems. This Special Issue focuses on publishing research that makes progress or improvements in rotordynamic analysis, modelling, monitoring, diagnosis, and life prognosis in rotating machinery.

In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following topics:

  • Rotordynamics in the electric propulsion system;
  • Rotordynamics in gas turbines;
  • Stochastich modelling of rotating machinery;
  • Bearings design for high-speed applications;
  • Modal updating in rotordynamics;
  • New methods for fault diagnosis techniques;
  • Electromagnetic bearings developments;
  • Journal bearing new developments;
  • Active vibration control in rotating machinery;
  • Elastohydrodynamic in bearings.

We welcome you to send a short abstract for feature paper submissions to the Editorial Office ([email protected]) before the formal submission of your manuscript. Selected planned papers can be published in full open access form, free of charge if accepted after a peer review.

Dr. Helio Fiori De Castro
Guest Editor

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.

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 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

32 pages, 66022 KiB  
Article
Reliability-Based Design Optimization Applied to a Rotor Supported by Hydrodynamic Bearings
by Helio Fiori de Castro, Eduardo Henrique de Paula and Laís Bittencourt Visnadi
Machines 2024, 12(4), 233; https://doi.org/10.3390/machines12040233 - 2 Apr 2024
Cited by 1 | Viewed by 1066
Abstract
Rotating machines are an important part of industrial equipment. It is essential to improve their performance while reducing the manufacturing, operating, and maintenance costs. Ensuring their reliability is also crucial because a machine breakdown can result in significant costs and potential environmental and [...] Read more.
Rotating machines are an important part of industrial equipment. It is essential to improve their performance while reducing the manufacturing, operating, and maintenance costs. Ensuring their reliability is also crucial because a machine breakdown can result in significant costs and potential environmental and safety damage. Reliability-based optimization is an approach that aims to find an optimal and robust design that guarantees a machine’s reliability. In this study, we focused on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings. We considered the materials’ elastic moduli, density, and bearing clearance as uncertain parameters. Our goal was to ensure 99% reliability regarding both the vibration amplitude and stability threshold. To model the machine, we used the finite element method and represented the bearings using stiffness and damping coefficients, considering the linear short bearing model. Due to the complexity of the model, we employed surrogate models to solve the reliability-based optimization problem. Our results showed that the optimization problem could be solved successfully using Kriging, polynomial chaos expansion, and polynomial chaos Kriging. Full article
(This article belongs to the Special Issue New Approaches in Rotating Machinery Modelling, Analysis and Monitoring)
►▼ Show Figures

Figure 1

18 pages, 5149 KiB  
Article
An Adaptive Model-Based Approach to the Diagnosis and Prognosis of Rotor-Bearing Unbalance
by Banalata Bera, Shyh-Chin Huang, Mohammad Najibullah and Chun-Ling Lin
Machines 2023, 11(10), 976; https://doi.org/10.3390/machines11100976 - 21 Oct 2023
Cited by 2 | Viewed by 2088
Abstract
Rotating machinery is the fundamental component of almost all industrial frameworks. Therefore, prognostics and health management (PHM) have emerged as crucial requirements for effectively managing and sustaining various systems in a timely manner. The unbalanced fault has been recognized as a significant contributing [...] Read more.
Rotating machinery is the fundamental component of almost all industrial frameworks. Therefore, prognostics and health management (PHM) have emerged as crucial requirements for effectively managing and sustaining various systems in a timely manner. The unbalanced fault has been recognized as a significant contributing factor in the development of faults in rotor-bearing systems, eventually causing failure. Thus, it is essential to monitor the unbalance and maintain it within acceptable bounds in order to guarantee the system’s proper operation. Most approaches to the rotor’s unbalance monitoring are model-based instead of data-driven due to the shortage of faulted data. In a derived model-based approach, proper identification of the model’s parameters, e.g., bearing parameters, always plays a very crucial role. Nonetheless, the identified model’s parameters in their initial state would inevitably degenerate during a long-term operation because of aging or environmental changes, such that they are no longer well representative of the real system. In this context, this paper offers an adaptive model-based approach for the assessment of unbalance faults developing over days in a rotor-bearing model. The model is adaptive in the sense that it automatically adjusts its parameters so that they are more closely aligned with the real system. A particle swarm optimization (PSO) scheme is utilized in the parameter identification process. The residual serves as the index for initiating the adaptive process when it is greater than a preset percentage. Individual feature errors work as a gauge to determine which bearing parameters need to be reevaluated. A set of 16-month operational data from a local petrochemical company is used to validate the approach. The unbalanced deterioration trend is evaluated, and results from the adaptive methodology are assessed to show its superiority over the original one. It is also observed that the model’s capacity to anticipate unbalance is greatly enhanced by the adaptive strategy. Finally, future unbalances are explored to show its capacity for continuous monitoring-based maintenance solutions. Full article
(This article belongs to the Special Issue New Approaches in Rotating Machinery Modelling, Analysis and Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop