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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-fc347061-2c21-4ae3-8640-b6d051ba8df4

Czasopismo

Archives of Civil and Mechanical Engineering

Tytuł artykułu

Application of selected Levy processes for degradation modelling of long range mine belt using real-time data

Autorzy Vališ, D.  Mazurkiewicz, D. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN When analysing big data generated by a typical diagnostic system, the maintenance operator has to deal with several problems, including a substantial number of data appearing every second. Maintenance systems, especially those in mining industry additionally require the operator to make reliable predictions and decisions under uncertainty. All this create so called information overload problem, which can be solved in data mining with the use of existing data reduction techniques. Unfortunately, with complex mining machinery operating under diverse conditions more advanced approaches are needed. Effective solutions can be found among non-trivial degradation assessment techniques provided which shall be properly applied. This work proposes new methods to modelling specific system degradation and prognosis for system failure occurrence. The approach presented here does not rely on typical statistical assumptions. This paper relates to mathematical modelling of real diagnostic data with the use of selected stochastic processes – types of Wiener process and Ornstein–Uhlenbeck process. The main novelty and contribution is in the specific forms of above mentioned processes, in the ways how the process parameters were estimated and also in realistic correlation of proposed models to the studied system. Simulated and real case results show that the proposed robust functional analysis reduces bias and provides more accurate false fault detection rates, as compared to the previous method. We hope the outcomes provide applicable inputs for more effective principles of system operation, predictive maintenance policy and risk assessment.
Słowa kluczowe
PL ocena niezawodności   eksploracja danych   konserwacja  
EN reliability assessment   levy diffusion process   predictive maintenance   data mining  
Wydawca Elsevier
Czasopismo Archives of Civil and Mechanical Engineering
Rocznik 2018
Tom Vol. 18, no. 4
Strony 1430--1440
Opis fizyczny Bibliogr. 38 poz., rys., tab., wykr.
Twórcy
autor Vališ, D.
  • University of Defence, Kounicova 65, 662 10 Brno, Czech Republic
  • University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland
autor Mazurkiewicz, D.
Bibliografia
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[10] A. Eiceman, J. Gardea-Torresdey, Data Reduction in Gas Chromatography. Encyclopedia of Analytical Chemistry, John Wiley & Sons, Ltd., 2006.
[11] A. Burduk, D. Mazurkiewicz, Intelligent systems In production engineering and maintenance – ISPEM 2017, in: A. Burduk, D. Mazurkiewicz (Eds.), Proceedings of the First International Conference on Intelligent Systems In Production Engineering and Maintenance ISPEM 2017. Advances in Intelligent Systems and Computing, vol. 637, 2018, http://dx.doi.org/10.1007/978-3-319-64465-3.
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[18] D. Marasova, L. Ambrisko, M. Andrejiova, A. Grincova, Examination of the process of damaging the top covering layer of a conveyor belt applying the FEM, Measurement 112 (2017) (2017) 47–52. , http://dx.doi.org/10.1016/j. measurement.2017.08.016.
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[21] N. Goodman, Application of Data Mining Algorithms for the Improvement and Synthesis of Diagnostic Metrics for Rotating Machinery, (Ph.D. dissertation), University of South Carolina, 2011.
[22] A. Glowacz, Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM, Eksploatacja i Niezawodnosc (Maintenance Reliabil.), 17 (4) (2015) 569–574. , http://dx.doi.org/10.17531/ein.2015.4.12.
[23] A. Glowacz, Diagnostics of rotor damages of three-phase induction motors using acoustic signals and SMOFS-20-EXPANDED, Arch. Acoust. 41 (3) (2016) 507–515.
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Uwagi
PL Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-fc347061-2c21-4ae3-8640-b6d051ba8df4
Identyfikatory
DOI 10.1016/j.acme.2018.05.006