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Machines
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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: closed (15 October 2024) | Viewed by 17319

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

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Published Papers (3 papers)

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Research

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14 pages, 2798 KiB  
Article
Investigation of Engine Lubrication Oil Quality Using a Support Vector Machine and Electronic Nose
by Ali Adelkhani and Ehsan Daneshkhah
Machines 2025, 13(2), 121; https://doi.org/10.3390/machines13020121 - 6 Feb 2025
Cited by 1 | Viewed by 600
Abstract
Monitoring the quality of engine oil improves engine efficiency and reduces engine maintenance costs. Several methods have been proposed for this purpose; however, most of them take too long to test oil quality. This paper introduces a fast, simple, and accurate method to [...] Read more.
Monitoring the quality of engine oil improves engine efficiency and reduces engine maintenance costs. Several methods have been proposed for this purpose; however, most of them take too long to test oil quality. This paper introduces a fast, simple, and accurate method to determine oil quality using an electronic nose and artificial intelligence. The TU5 engine and 10-40W “Behran Super Pishtaz” engine oil were used in the experiments. Tests were conducted at six different quality levels. Oil properties such as viscosity, density, flash point, and freezing point were measured at each level. Additionally, oil smell signals were recorded using an olfactory machine at these quality levels. The fraction method was employed to adjust the sensors’ responses. Five statistical features were extracted from each signal, and these features were used to train and test a support vector machine (SVM) for classifying oil quality using the five-fold cross-validation method. The results indicated a statistically significant change in viscosity and density with variations in oil quality. The density increased as the quality decreased. Viscosity, however, initially decreased and then increased at later stages. An analysis of the sensory outputs revealed that changes in oil quality also affected these outputs, with the most pronounced sensitivity observed in the MQ135 and MQ138 sensors. The final accuracies of the SVM in classifying oil quality were 68.22%, 85.86%, and 95.44% for linear, radial basis function (RBF), and polynomial kernels, respectively. The SVM sensitivities for oil qualities A, B, C, D, E, and F were 97.99%, 97.37%, 95.51%, 92.67%, 94.48%, and 94.59%, respectively. 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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28 pages, 4981 KiB  
Article
Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making
by Abdullahi Abdulkarim Daya and Iraklis Lazakis
Machines 2024, 12(5), 294; https://doi.org/10.3390/machines12050294 - 27 Apr 2024
Cited by 2 | Viewed by 2690
Abstract
Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to [...] Read more.
Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome. Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies. 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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Review

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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 - 1 Jul 2023
Cited by 21 | Viewed by 13173
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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