ISSN 1507-2711
ISSN online 2956-3860

JOURNAL DOI: dx.doi.org/10.17531/ein

JCR Journal Profile


Członek(Member of): Europejskiej Federacji Narodowych Towarzystw Eksploatacyjnych  - European Federation of National Maintenance Societies  Wydawca(Publisher):Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne (Warszawa) - Polish Maintenance Society (Warsaw)   Patronat Naukowy(Scientific supervision): Polska Akademia Nauk o/Lublin  - Polish Akademy of Sciences Branch in Lublin  Członek(Member of): Europejskiej Federacji Narodowych Towarzystw Eksploatacyjnych  - European Federation of National Maintenance Societies


 We verify submissions originality with the use of iThenticate plagiarism checker


 All accepted articles are published Open Access under the Creative Commons Licence: CC-BY 4.0

Publisher:
Polish Maintenance Society
(Warsaw)

Scientific supervision:
Polish Academy of Sciences Branch in Lublin

Member of:
European Federation
of National Maintenance Societies


Attention!

In accordance with the requirements of citation databases, proper citation of publications appearing in our Quarterly should include the full name of the journal in Polish and English without Polish diacritical marks, i.e. "Eksploatacja i Niezawodnosc – Maintenance and Reliability".


 

Submission On-Line


The average number of weeks from article submission to the final decision: 4 weeks




http://scientific.thomsonreuters.com/cgi-bin/jrnlst/jloptions.cgi?PC=D

http://www.thomsonreuters.com/products_services/scientific/Journal_Citation_Reports

http://doaj.org

http://infobaseindex.com

http://www.info.scopus.com/why-scopus/publishers/?url=detail/what/publishers/

http://www.ebsco.com


MOST CITED

Update: 2021-07-01

1. COMPUTER-AIDED MAINTENANCE AND RELIABILITY MANAGEMENT SYSTEMS FOR CONVEYOR BELTS
By: Mazurkiewicz, Dariusz

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume: 16   Issue: 3   Pages: 377-382   Published: 2014

Times Cited: 59
2. ON APPROACHES FOR NON-DIRECT DETERMINATION OF SYSTEM DETERIORATION
By: Valis, David; Koucky, Miroslav; Zak, Libor

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume 14, Issue: 1   Pages: 33-41   Published: 2012

Times Cited: 53
3. A NEW FAULT TREE ANALYSIS METHOD: FUZZY DYNAMIC FAULT TREE ANALYSIS
By: Li, Yan-Feng; Huang, Hong-Zhong; Liu, Yu; et al.

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume 14, Issue: 3 Pages: 208-214 Published: 2012

Times Cited: 51
4. INNOVATIVE METHODS OF NEURAL RECONSTRUCTION FOR TOMOGRAPHIC IMAGES IN MAINTENANCE OF TANK INDUSTRIAL REACTORS
By: Rymarczyk, Tomasz; Klosowski, Grzegorz

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume: 21 Issue: 2 Pages: 261-267 Published: 2019

Times Cited: 50
5. APPLICATION OF NEURAL RECONSTRUCTION OF TOMOGRAPHIC IMAGES IN THE PROBLEM OF RELIABILITY OF FLOOD PROTECTION FACILITIES
By: Rymarczyk, Tomasz; Klosowski, Grzegorz

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume: 20 Issue: 3 Pages: 425-434 Published: 2018

Times Cited: 45
6. ASSESSMENT MODEL OF CUTTING TOOL CONDITION FOR REAL-TIME SUPERVISION SYSTEM
By: Kozlowski, Edward; Mazurkiewicz, Dariusz; Zabinski, Tomasz; Prucnal, Slawomir; Sep, Jaroslaw

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume: 21 Issue: 4 Pages: 679-685 Published: 2019

Times Cited: 40
7. PREDICTING THE TOOL LIFE IN THE DRY MACHINING OF DUPLEX STAINLESS STEEL
By: Krolczyk, Grzegorz; Gajek, Maksymilian; Legutko, Stanislaw

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume: 15 Issue: 1 Pages: 62-65 Published: 2013

Times Cited: 39
8. MAINTENANCE DECISION MAKING BASED ON DIFFERENT TYPES OF DATA FUSION
By: Galar, Diego; Gustafson, Anna; Tormos, Bernardo; et al.
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 
Volume 14, Issue: 2   Pages: 135-144   Published:2012

Times Cited: 38
9. TESTS OF EXTENDABILITY AND STRENGTH OF ADHESIVE-SEALED JOINTS IN THE CONTEXT OF DEVELOPING A COMPUTER SYSTEM FOR MONITORING THE CONDITION OF BELT JOINTS DURING CONVEYOR OPERATION
By: Mazurkiewicz, Dariusz

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Issue: 3 Pages: 34-39 Published: 2010

Times Cited: 37
10. RELIABILITY ANALYSIS OF RECONFIGURABLE MANUFACTURING SYSTEM STRUCTURES USING COMPUTER SIMULATION METHODS
By: Gola, Arkadiusz

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume 21, Issue: 1, Pages: 90-102, Published: 2019

Times Cited: 36

 

 



Task „Implementation of procedures ensuring  the originality of scientific papers published in the quarterly „Eksploatacja i Niezawodność – Maintenance and Reliability” financed under contract 532/P-DUN/2018 from the funds of the Minister of Science and Higher Education for science dissemination activities.


LAST ADDED

An attempt at applying machine learning in diagnosing marine ship engine turbochargers

DOI: 10.17531/ein.2022.4.19

Article citation info: 
Adamkiewicz A, Nikończuk P. An attempt at applying machine learning in diagnosing marine ship engine turbochargers. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (4): 795–804, http://doi.org/10.17531/ein.2022.4.19

 

Abstract: 

The article presents a diagnosis of turbochargers in the supercharging systems of marine engines in terms of maintenance decisions. The efficiency of turbocharger rotating machines was defined. The operating parameters of turbocharging systems used to monitor the correct operation and diagnose turbochargers were identified. A parametric diagnostic test was performed. Relationships between parameters for use in machine learning were selected. Their credibility was confirmed by the results of the parametric test of the turbocharger system and the main engine, verified by the coefficient of determination. A particularly good fit of the describing functions was confirmed. As determinants of the technical condition of a turbocharger, the relationship between the rotational speed of the engine shaft, the turbocharger rotor assembly and the charging air pressure was assumed. In the process of machine learning, relationships were created between the rotational speed of the engine shaft and the boost pressure, and the indicator of the need for maintenance. The accuracy of the maintenance decisions was confirmed by trends in changes in the efficiency of compressors.

Comprehensive importance analysis for repairable system components based on the GO method

DOI: 10.17531/ein.2022.4.18

Article citation info: 
Jiang X, Wang Y, Li J, Ye L. Comprehensive importance analysis for repairable system components based on the GO method. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (4): 785–794, http://doi.org/10.17531/ein.2022.4.18

 

Abstract: 

In order to effectively improve the reliability level of the permanent magnet synchronous motor (PMSM) drive system of electric aircraft, a component importance analysis based on the GO method for the repairable systems is proposed. Firstly, the system reliability model GO diagram is established according to the hardware schematic diagram of the PMSM drive system. Secondly, the steady-state availability and failure importance of the components are calculated. In addition, the criteria importance through intercriteria correlation (CRITIC) is adopted to determine the objective weights of steady-state availability and failure importance. The combined weighting is employed to obtain the importance of key components. Meanwhile, a system redundancy design based on the importance of components is proposed to provide data support for the design of the system. Finally, the feasibility and effectiveness of the proposed method are evaluated by an example of an electric aircraft PMSM drive system. This method provides a supporting basis for the optimization design of the entire system.

Identification of crashworthiness indicators of column energy absorbers with triggers in the form of cylindrical embossing on the lateral edges using artificial neural networks

DOI: 10.17531/ein.2022.4.20

Article citation info: 
Ferdynus M, Gajewski J. Identification of crashworthiness indicators of column energy absorbers with triggers in the form of cylindrical embossing on the lateral edges using artificial neural networks. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (4): 805–821, http://doi.org/10.17531/ein.2022.4.20

 

Abstract: 

The paper presents the possibility of neural network application in order to identify the most advantageous design variants of column energy absorbers in terms of the achieved energy absorption indicators. Design variants of the column energy absorber made of standard thin-walled square aluminium profile with triggers in the form of four identical cylindrical embossments on the lateral edges were considered. These variants differ in the diameter of the trigger, its depth and position. The geometrical parameters of the trigger are crucial for the energy absorption performance of the energy absorber. The following indicators are studied: PCF (Peak Crushing Force), MCF (Mean Crushing Force), CLE (Crash Load Efficiency), SE (Stroke Efficiency) and TE (Total Efficiency). On the basis of numerical studies validated by experimentation, a neural network has been created with the aim of predicting the above-mentioned indices with an acceptable error for an energy absorber with the trigger of specified geometrical parameters and position. The paper demonstrates that the use of an effective multilayer perceptron can successfully speed up the design process, saving time on multivariate time-consuming analyses.

Selective maintenance optimization with stochastic break duration based on reinforcement learning

DOI: 10.17531/ein.2022.4.17

Article citation info: 
Liu Y, Qian X. Selective maintenance optimization with stochastic break duration based on reinforcement learning. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2022; 24 (4): 771–784, http://doi.org/10.17531/ein.2022.4.17

 

Abstract: 

For industrial and military applications, a sequence of missions would be performed with a limited break between two adjacent missions. To improve the system reliability, selective maintenance may be performed on components during the break. Most studies on selective maintenance generally use minimal repair and replacement as maintenance actions while break duration is assumed to be deterministic. However, in practical engineering, many maintenance actions are imperfect maintenance, and the break duration is stochastic due to environmental and other factors. Therefore, a selective maintenance optimization model is proposed with imperfect maintenance for stochastic break duration. The model is aimed to maximize the reliability of system successfully completing the next mission. The reinforcement learning(RL) method is applied to optimally select maintenance actions for selected components. The proposed model and the advantages of the RL are verified by three case studies verify.


SELECT PUBLICATION YEAR