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Machines
Special Issues
Condition-Based Maintenance, Instrumentation and Data Analysis Methods Aiming Efficient Operation...
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Condition-Based Maintenance, Instrumentation and Data Analysis Methods Aiming Efficient Operation of Internal Combustion Engines

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 9294

Special Issue Editors

Dr. Wilson Cesar Sant'Ana

E-Mail Website
Guest Editor
Institute of Systems Engineering and Information Technology (IESTI), Federal University of Itajuba (UNIFEI), Itajuba 37500-903, Brazil
Interests: condition-based maintenance; frequency response analysis of electric machinery; power electronics; digital signal processing and control systems
Prof. Dr. Helcio Francisco Villa-Nova

E-Mail Website
Guest Editor
Institute of Mechanical Engineering (IEM), Federal University of Itajuba (UNIFEI), Itajuba 37500-903, Brazil
Interests: combustion; generation-propulsion; turbomachines; computational fluid dynamics and renewable energy
Dr. Erik Leandro Bonaldi

E-Mail Website
Guest Editor
R&D Department, Gnarus Institute, Itajubá 37502-485, Brazil
Interests: industrial electronic automation; predictive maintenance; artificial intelligence methodologies

Special Issue Information

Dear Colleagues,

Despite the ever increasing interest of renewable energy sources, power plants based on Internal Combustion Engines (ICEs) still play an important role in power systems—due to requirements of fast dispatch of energy (either to supply peaks in demand or to compensate for intermittency of renewable sources). Hence, in order to reduce emissions and to reduce consumption of fuels, the operation of these engines with maximized efficiencies is paramount.

We are pleased to invite you to contribute with your research (either from the academia or industry) on topics related to the increase of efficiency of ICEs (not only in power generation but also in transportation). We are looking for papers focusing on (but not limited to) condition-based maintenance, sensors and instrumentation, signal processing techniques, and algorithms aiming to improve the operation of the ICEs. Additionally, papers related to the optimized combustion of fuels are welcomed—which may include online characterization of fuels and detection of contaminants and counterfeit/adulteration. 

Dr. Wilson Cesar Sant'Ana
Prof. Dr. Helcio Francisco Francisco Villa-Nova
Dr. Erik Leandro Leandro Bonaldi
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

  • condition-based maintenance
  • efficiency
  • instrumentation
  • internal combustion engines
  • thermal power plants
  • transportation

Published Papers (1 paper)

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Review

22 pages, 1243 KiB  
Review
A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions
by Hla Gharib and György Kovács
Machines 2023, 11(7), 695; https://doi.org/10.3390/machines11070695 - 01 Jul 2023
Cited by 4 | Viewed by 8860
Abstract
Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, [...] Read more.
Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning. Full article
(This article belongs to the Special Issue Condition-Based Maintenance, Instrumentation and Data Analysis Methods Aiming Efficient Operation of Internal Combustion Engines)
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