MOST CITED
Update: 2021-07-01
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.
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Specification of estimation of a passenger car ride smoothness under various exploitation conditionsDOI: 10.17531/ein.2021.4.14
Issue: Article citation info: Vaičiūnas G, Steišūnas S, Bureika G. Specification of estimation of a passenger car ride smoothness under various exploitation conditions. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 719–725, http://doi.org/10.17531/ein.2021.4.14.
Abstract: The stability and smoothness of rolling stock running could be defined accurately by universal Sperling's comfort index. The divergences of variation of Sperling's comfort index of a passenger car under specific operating conditions of running gear are examining in this paper. Numerical simulations of a passenger car running with independently rotating wheels under various conditions have been performing. Gained results showed that divergences of the Sperling's comfort index variation are particularly significant due to running gear component oscillations in the horizontal plane (lateral direction). A field experiment of a passenger car with a solid (traditional) wheelset with a flat running surface proved this hypothesis. The obtained results of this experiment confirmed this assumption. Therefore, the study of the regularities of lateral oscillations of a passenger car is the logical direction of further research. |
The study on the automated storage and retrieval system dependabilityDOI: 10.17531/ein.2021.4.13
Issue: Article citation info: Lewczuk K. The study on the automated storage and retrieval system dependability. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 709–718, http://doi.org/10.17531/ein.2021.4.13.
Abstract: Automated storage systems have become the basis of warehouse logistics. The article presents a discussion on the reliability and dependability of Automated Storage and Retrieval Systems (ASRS), which are perceived as solutions with high technical reliability. Still, their role in the dependability of the entire warehouse system is to be discussed. The concepts of reliability and dependability in logistics systems like ASRS are defined, and a literature review in this area is presented. On this basis, the factors influencing the dependability of ASRS are discussed in a way not present in the discussion on this topic so far. Then, the ASRS simulation model (based on FlexSim simulation software) is presented. The model tests the influence of ASRS configuration and assigned resources on the dependability of the warehouse as a master system. The summary includes observations on defining the reliability and dependability of ASRS. |
Application of machine learning and rough set theory in lean maintenance decision support system developmentDOI: 10.17531/ein.2021.4.12
Issue: Article citation info: Antosz K, Jasiulewicz-Kaczmarek M, Paśko Ł, Zhang C, Wang S. Application of machine learning and rough set theory in lean maintenance decision support system development. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 695–708, http://doi.org/10.17531/ein.2021.4.12.
Abstract: Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance. |
Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learningDOI: 10.17531/ein.2021.4.11
Issue: Article citation info: Wu C, Sun H, Lin S, Gao S. Remaining useful life prediction of bearings with different failure types based on multi-feature and deep convolution transfer learning. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2021; 23 (4): 685–694, http://doi.org/10.17531/ein.2021.4.11.
Abstract: The accurate prediction of the remaining useful life (RUL) of rolling bearings is of immense importance in ensuring the safe and smooth operation of machinery and equipment. Although the prediction accuracy has been improved by a predictive model based on deep learning, it is still limited in engineering because lots of models use single-scale features to predict and assume that the degradation data of each bearing has a consistent distribution. In this paper, A deep convolutional migration network based on spatial pyramid pooling (SPP-CNNTL) is proposed to obtain higher prediction accuracy with self-extraction of multi-feature from the original vibrating signal. And to consider the differences of the data distribution in different failure types, transfer learning (TL) added with maximum mean difference (MMD) measurement function is used in the RUL prediction part. Finally, the data of IEEE PHM 2012 Challenge is used for verification, and the results show that the method in this paper has high prediction accuracy. |
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