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

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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.
Rocznik
Strony
1430--1440
Opis fizyczny
Bibliogr. 38 poz., rys., tab., wykr.
Twórcy
autor
  • University of Defence, Kounicova 65, 662 10 Brno, Czech Republic
  • University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland
  • Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Bibliografia
  • [1] K. Antosz, C.R.M. Ratnayake, Machinery classification and prioritization: empirical models and AHP based approach for effective preventive maintenance, in: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2016, 1380–1386. , http://dx.doi.org/ 10.1109/IEEM.2016.7798104.
  • [2] E. Auschitzky, M. Hammer, A. Rajagopaul, How Big Data Can Improve Manufacturing, McKinsey& Company, Insights & Publications, 2014 http://www.mckinsey.com/insights/operations/how_big_data_can_improve_manufacturing (accessed 20.11.17).
  • [3] M. Jasiulewicz-Kaczmarek, Practical aspects of the application of RCM to select optimal maintenance policy of the production line, in: T. Nowakowski, M. Mlynczak, A. Jodejko-Pietruczuk, et al. (Eds.), Conference Proceedings of the European Safety and Reliability Conference (ESREL), 2015, 1187–1195. , http://dx.doi.org/10.1201/b17399-165.
  • [4] A. Katunin, Stone impact damage identification in composite plates using modal data and quincunx wavelet analysis, Arch. Civil Mech. Eng. 15 (1) (2015) 251–261. , http://dx.doi.org/10.1016/j.acme.2014.01.010.
  • [5] E. Kozłowski, B. Kowalska, D. Kowalski, D. Mazurkiewicz, Water demand forecasting by trend and harmonic analysis, Arch. Civil Mech. Eng. 18 (1) (2018) 140–148. , http://dx.doi.org/10.1016/j.acme.2017.05.006.
  • [6] D. Mazurkiewicz, A. Rudawska, Inspirations for Innovation: The Causes and Effects of Progress in Production Engineering, Lublin University of Technology, Lublin, 2016.
  • [7] E. Kozłowski, Analysis and Identification of Time Series (In Polish). A Monograph, Lublin University of Technology Publishing Office, Lublin, 2015.
  • [8] D. Uhm, S. Jun, S.-J. Lee, A classification method using data reduction, Int. J. Fuzzy Logic Intell. Syst. 1 (12) (2012) 1–5.
  • [9] K. Choi, J. Luo, K.R. Pattipati, S.M. Namburu, L. Qiao, S. Chigusa, Data Reduction Techniques for Intelligent Fault Diagnosis in Automotive Systems. Autotestcon IEEE, Paper 283655, 2006.
  • [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.
  • [12] A. Loska, W. Paszkowski, SmartMaintenance – the concept of supporting the exploitation decision-making process in the selected technical network system, in: A. Burduk, D. Mazurkiewicz (Eds.), Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017, Advances In Intelligent Systems and Computing, vol. 637, Springer International Publishing, 2018 64–73.
  • [13] B. De Jonge, R. Teunter, T. Tinga, The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance, Reliabil. Eng. Syst. Saf. 158 (2017) 21–30. , http://dx.doi.org/10.1016/j.ress.2016.10.002.
  • [14] W. Donat, K. Choi, W. An, S. Singh, P. Pattipati, Data visualization, data reduction and classifier fusion for intelligent fault detection and diagnosis in gas turbine engines, in: Proceedings of GT2007: Turbo Expo 2007: Power for Land, Sea and Air, May 14–17, 2007, Montreal, Canada, 2007.
  • [15] E. Gilabert, S. Fernandez, A. Arnaiz, E. Konde, Simulation of predictive maintenance strategies for cost-effectiveness analysis, Proc. Inst. Mech. Engrs. Part B J. Eng. Manuf. (2015), 0954405415578594.
  • [16] I. Mustakerov, D. Borissova, An Intelligent Approach to Optimal Predictive Maintenance Strategy Defining. 978-1-4799-0661-1/13, 2013 IEEE, 2013 http://iict.bas.bg/acomin/docs/sci-forums/19-21-June-2013/An%20Intelligent%20Approach%20to%20Optimal%20Predictive%20Maintenance %20Strategy%20Defining.pdf (accessed 20.11.17).
  • [17] S. Oelker, M. Lewandowski, Artificial Intelligence and Predictive Maintenance. Project preInO Combines Artificial Intelligence with Automatic Self-Organization to Optimize Maintenance and Repair at Sea by Setting in Motion the Corresponding Personnel, Material and Spare Parts Logistics, Marine Maintenance Technology International, 2014.
  • [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.
  • [19] M.E. Zamiralova, G. Lodewijks, Review of the troughability test ISO 703 for quantifying a uniform transverse Bendig stiffness for conveyor belts, Arch. Civil Mech. Eng. 17 (2) (2017) 249–270. , http://dx.doi.org/10.1016/j.acme.2016.10.007.
  • [20] R. Ahmadi, M. Fouladirad, Maintenance planning for a deteriorating production process, Reliabil. Eng. Syst. Saf. 159 (2017) 108–118. , http://dx.doi.org/10.1016/j.ress.2016.11.001.
  • [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.
  • [24] A. Glowacz, Z. Glowacz, Diagnosis of the three-phase induction motor using thermal imaging, Infrared Phys. Technol. 81 (2017) 7–16.
  • [25] D. Mazurkiewicz, Tests of extendability and strength of adhesive-sealed joints in the context of developing a computer system for monitoring the condition of belt joint during conveyor operation, Eksploatacja i Niezawodność (Maintenance Reliabil.), 3 (47) (2010) 34–39.
  • [26] D. Mazurkiewicz, Computer-aided maintenance and reliability management systems for conveyor belts, Eksploatacja i Niezawodnosc (Maintenance Reliabil.), 16 (3) (2014) 377–382.
  • [27] D. Mazurkiewicz, The future of production engineering, in: D. Mazurkiewicz, A. Rudawska (Eds.), Inspirations for Innovation: The Causes and Effects of Progress In Production Engineering, A Monograph, Lublin University of Technology Publishing Office, Lublin, 2016 33–44.
  • [28] H. Baji, C.Q. Li, S. Scicluna, J. Dauth, Risk-cost optimised maintenance strategy for tunnel structures, Tunnell. Underground Space Technol. 69 (2017) 72–84. , http://dx.doi.org/10.1016/j.tust.2017.06.008.
  • [29] Y. Dijoux, M. Fouladirad, T.N. Dinh, Statistical inference for imperfect maintenance models with missing data, Reliabil. Eng. Syst. Saf. 154 (2016) 84–96. , http://dx.doi.org/10.1016/j. ress.2016.05.017.
  • [30] G. Jin, D.E. Matthews, Z. Zhou, A Bayesian framework for online degradation assessment and residual life prediction of secondary batteries in spacecraft, Reliabil. Eng. Syst. Saf. 113 (2013) 7–20. , http://dx.doi.org/10.1016/j.ress.2012.12.011.
  • [31] M.G. Baydogan, G. Runger, Learning a symbolic representation for multivariate time series classification, Data Mining Knowl. Discov. 29 (2) (2015) 400–422. , http://dx.doi.org/10.1007/s10618-014-0349-y.
  • [32] H.B. Dai, F.D. Zhu, E.P. Lim, H. Pang, Detecting anomalny collections using extreme feature ranks, Data Mining Knowl. Discov. 29 (3) (2015) 689–731. , http://dx.doi.org/10.1007/s10618-014-0360-3.
  • [33] S.K. Gupta, D. Phung, B. Adams, S. Venkatesh, Regularized nonnegative shared subspace learning, Data Mining Knowl. Discov. 26 (1) (2013) 57–97. , http://dx.doi.org/10.1007/s10618-011-0244-8.
  • [34] D. Vališ, M. Koucký, L. Žák, On approaches for non-direct determination of system deterioration, Eksploatacja i Niezawodnosc (Maintenance Reliabil.), 14 (1) (2012) 33–41.
  • [35] D. Vališ, L. Žák, Failure prediction of diesel engine based on occurrence of selected wear particles in oil, Eng. Fail. Anal. 56 (2015) 501–511.
  • [36] B. Oksendal, Stochastic Differential Equations – An Introduction with Applications, Springer, London, 2000.
  • [37] D. Vališ, L. Žák, O. Pokora, P. Lánský, Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance, Reliabil. Eng. Syst. Saf. 145 (1) (2016) 231–242. , http://dx.doi.org/10.1016/j. ress.2015.07.026.
  • [38] D. Vališ, L. Žák, O. Pokora, Perspective Approach in Using Anti-oxidation and Anti-wear Particles From Oil to Estimate Residual Technical Life of a System, Tribology International, 2017 0301-679X.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fc347061-2c21-4ae3-8640-b6d051ba8df4
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