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Electronics
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Monitoring, Diagnosis, and Prognostics for Power Industry Devices
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Monitoring, Diagnosis, and Prognostics for Power Industry Devices

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 1693

Special Issue Editors

Prof. Dr. Michał Pająk

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Guest Editor
Faculty of Mechanical Engineering, Casimir Pulaski Radom University, ul. Stasieckiego 54, 26-600 Radom, Poland
Interests: artificial intelligence; machine learning; operation and maintenance of complex technical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dragutin Lisjak

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Guest Editor
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ivana Lučića 5, 10000 Zagreb, Croatia
Interests: diagnostics; maintenance and computer modelling of complex technical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Łukasz Muślewski

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Guest Editor
Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, 7 Kaliskiego Avenue, 85-796 Bydgoszcz, Poland
Interests: efficiency; reliability; safety of complex operation systems of technical devices

Special Issue Information

Dear Colleagues,

Recent decades have seen an increase in the complexity of production systems due to higher requirements for profitability. This has made the task of ensuring high reliability of devices operation much more complicated than in the past. One of the most complex technical systems can be found in the energy industry. In addition, power systems are crucial systems of strategic importance for the national economy. In the case of such systems, the consequences of failure can be very costly or pose a threat to human health and life. Therefore, one of the main tasks of their operation and maintenance is to minimize the risk of inability state occurrence. So far, this task has been accomplished by the implementation of the preventive maintenance strategy, which resulted in the loss of the operational potential. To improve the situation, a strategy of maintenance according to technical condition should be applied, but this was hampered by the high cost of continuous diagnostic systems and the lack of measurements. Currently, thanks to the progress in information technology, automation and telecommunications, the industry 4.0 revolution is taking place. The Internet of Things, Digital Twins technology, Artificial Intelligence and Machine Learning support the intelligent processing of huge amounts of operational data of technological processes. This is a completely new basis for monitoring, diagnostics and prognostics of energy devices. In order to create a space for sharing experiences in this new and dynamically developing research field, the Special Issue is devoted to the diagnosis of power systems and devices. Topics of interest for publication include, but are not limited to:

  • Power boilers diagnostics;
  • Combustion engines diagnostics;
  • Steam, gas, water and wind turbines diagnostics;
  • Power generators diagnostics; 
  • Energy storage systems diagnostics;
  • Operation and maintenance control of heat power plants;
  • Operation and maintenance control of nuclear power plants;
  • Operation and maintenance control of renewable energy systems.

Prof. Dr. Michał Pająk
Prof. Dr. Dragutin Lisjak
Prof. Dr. Łukasz Muślewski
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. Electronics is an international peer-reviewed open access semimonthly 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

  • monitoring
  • diagnostics
  • prognostic
  • operation
  • maintenance
  • power industry
  • complex technical systems

Published Papers (2 papers)

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Research

19 pages, 582 KiB  
Article
Distinction between Arcing Faults and Oil Contamination from OLTC Gases
by Sergio Bustamante, Jose L. Martinez Lastra, Mario Manana and Alberto Arroyo
Electronics 2024, 13(7), 1338; https://doi.org/10.3390/electronics13071338 - 02 Apr 2024
Viewed by 411
Abstract
Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer’s lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration [...] Read more.
Power transformers are the most important and expensive assets in high-voltage power systems. To ensure an adequate level of reliability throughout the transformer’s lifetime, its maintenance strategy must be well defined. When an incipient fault occurs in the transformer insulation, a gas concentration pattern, representative of the type of fault, is generated. Fault-identification methods use gas concentrations and their ratios to identify the type of fault. None of the traditional or new fault-identification methods attempt to detect transformer oil contamination from on-load tap changer (OLTC) gases. In this study, from dissolved gas analysis (DGA) samples of transformers identified as contaminated in a previous study, fault-identification methods based on graphical representations were used to observe the patterns of results. From such patterns, Duval’s triangle and pentagon methods were modified to include a new zone indicating oil contamination (OC) from OLTC gases. Finally, the proposed modifications were validated using 75 DGA samples extracted from previous studies that were identified as D1 or D2 faults or contaminated from OLTC. This validation showed that only 14.7% and 13.3% of the DGA samples fell within the new OC zone of the proposed triangle and pentagon, respectively. Full article
(This article belongs to the Special Issue Monitoring, Diagnosis, and Prognostics for Power Industry Devices)
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18 pages, 3711 KiB  
Article
Ship Diesel Engine Fault Diagnosis Using Data Science and Machine Learning
by Michał Pająk, Marcin Kluczyk, Łukasz Muślewski, Dragutin Lisjak and Davor Kolar
Electronics 2023, 12(18), 3860; https://doi.org/10.3390/electronics12183860 - 12 Sep 2023
Cited by 3 | Viewed by 908
Abstract
One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis [...] Read more.
One of the most important elements of the reliability structure of a motor vessel is its power subsystem, with the most crucial component being the engine. An engine failure excludes the ship from operation or significantly limits its operation. Therefore, accurate fault diagnosis should be a crucial issue for modern maintenance strategies. In mechanical engineering, the vibration and acoustic signals recorded during the operation of the device are the most meaningful data used to identify the reliability state. In this paper, a novel system-oriented method of reliability state identification is proposed. The method consists of the analysis of the vibration and noise signals collected on each of the engine cylinders using supervised machine learning. The main novelty of this method is data augmentation application and SVM classifier implementation. Due to these aspects, the method becomes robust in the case of poor-quality data or a limited and incomplete learning dataset. The quality of the proposed identification method was evaluated by addressing a new industrial issue (Sulzer 6AL20/24 marine engine reliability state identification). During the tests, the efficiency of the method was analyzed in the case of a complete learning data set (all types of inability states were presented in the learning data set) and an incomplete learning data set (in the testing data set, there were new types of inability states). As a result, in both cases, a very high (100%) identification accuracy of the reliability state and the type of inability state was obtained. This is a significant increase in accuracy (4.6% for the complete and 22% for the incomplete learning data set) in comparison to the efficiency of the same method without the use of machine learning and data science. Full article
(This article belongs to the Special Issue Monitoring, Diagnosis, and Prognostics for Power Industry Devices)
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