ISSN 1507-2711
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".


 

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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

A method for estimating the probability distribution of the lifetime for new technical equipment based on expert judgement

DOI: 10.17531/ein.2021.4.18

Article citation info: 
Andrzejczak K, Bukowski L. A method for estimating the probability distribution of the lifetime for new technical equipment based on expert judgement. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 757–769, http://doi.org/10.17531/ein.2021.4.18.

 

Abstract: 

Managing the exploitation of technical equipment under conditions of uncertainty requires the use of probabilistic prediction models in the form of probability distributions of the lifetime of these objects. The parameters of these distributions are estimated with the use of statistical methods based on historical data about actual realizations of the lifetime of examined objects. However, when completely new solutions are introduced into service, such data are not available and the only possible method for the initial assessment of the expected lifetime of technical objects is expert methods. The aim of the study is to present a method for estimating the probability distribution of the lifetime for new technical facilities based on expert assessments of three parameters characterizing the expected lifetime of these objects. The method is based on a subjective Bayesian approach to the problem of randomness and integrated with models of classical probability theory. Due to its wide application in the field of maintenance of machinery and technical equipment, a Weibull model is proposed, and its possible practical applications are shown. A new method of expert elicitation of probabilities for any continuous random variable is developed. A general procedure for the application of this method is proposed and the individual steps of its implementation are discussed, as well as the mathematical models necessary for the estimation of the parameters of the probability distribution are presented. A practical example of the application of the developed method on specific numerical values is also presented.

Remaining useful life prediction with insufficient degradation data based on deep learning approach

DOI: 10.17531/ein.2021.4.17

Article citation info: 
Lyu Y, Jiang Y, Zhang Q, Chen C. Remaining useful life prediction with insufficient degradation data based on deep learning approach. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 745–756, http://doi.org/10.17531/ein.2021.4.17.

 

Abstract: 

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

Influence of the movement of involute profile gears along the off-line of action on the gear tooth position along the line of action direction

DOI: 10.17531/ein.2021.4.16

Article citation info: 
Jedliński Ł. Influence of the movement of involute profile gears along the off-line of action on the gear tooth position along the line of action direction. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 736–744, http://doi.org/10.17531/ein.2021.4.16.

 

Abstract: 

When gears change their distance along the off-line of action (OLOA) direction, this affects the distance between the working surfaces of the meshing teeth along the line of action (LOA). This effect is usually neglected in studies. To include this effect precise equations are derived for spur gears. The analysis is carried out for the general case of spur gears with shifted profiles frequently used in the industry. The influence of OLOA gear displacement on LOA direction is also a function of gears parameters. An analysis is conducted, and the impact of parameters such as module, pressure angle, gear ratio, and the number of teeth is determined. As an example, a simulation of a 12 DOF analytical model is presented. The movement of gears along OLOA is caused by a frictional force that can be high during tooth degradation e.g. scuffing. Results show that when the movement of gears along the OLOA direction is significant, its influence on the distance between the mating teeth should not be neglected.

The post-warranty random maintenance policies for the product with random working cycles

DOI: 10.17531/ein.2021.4.15

Article citation info: 
Shang L, Wang H, Wu C, Cai Z. The post-warranty random maintenance policies for the product with random working cycles. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 726–735, http://doi.org/10.17531/ein.2021.4.15.

 

Abstract: 

Advanced sensors and measuring technologies make it possible to monitor the product working cycle. This means the manufacturer’s warranty to ensure reliability performance can be designed by monitoring the product working cycle and the consumer’s post-warranty maintenance to sustain the post-warranty reliability can be modeled by tracking the product working cycle. However, the related works appear seldom in existing literature. In this article, we incorporate random working cycle into warranty and propose a novel warranty ensuring reliability performance of the product with random working cycles. By extending the proposed warranty to the post-warranty maintenance, besides we investigate the postwarranty random maintenance policies sustaining the post-warranty reliability, i.e., replacement last (first) with preventive maintenance (PM). The cost rate is constructed for each post-warranty random maintenance policy. Finally, sensitivity of proposed warranty and investigated polices is analyzed. We discover that replacement last (first) with PM is superior to replacement last (first).


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