PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Maintenance of belt conveyors using an expert system based on fuzzy logic

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In recent years, conveyor belt transport systems have taken on a new significance due to numerous research studies on innovative design solutions. The application of these new developed solutions leads to considerable reduction in operational costs of transport systems, while ensuring their high reliability and service life at the same time. Nonetheless, there are still areas that pose challenge to both research and development. Typical challenges are analyzed in this paper. The solution to the problems of conveyor transport maintenance can be the implementation of a system for estimation of technical condition of conveyor belt joints. It serves as a second level safety diagnostic system for transport. Besides real-time measurements, the system enables a long-term analysis of historic data for every single joint that makes up the conveyor belt loop, from the moment of its manufacture to the final operation. The effectiveness of a conveyor belt diagnostic system primarily depends on the use of a decision supporting system. With adequate inference rules applied, this system would increase the effectiveness and shorten the time of decision-making as well as verify generated signals. The above tasks can be performed by a suitable expert system that predicts values of the analyzed time series, using the predicted values and inference rules to verify any potential false alarm signals at the same time. The idea and algorithm of such an expert system were presented in this article as well.
Rocznik
Strony
412--418
Opis fizyczny
Bibliogr. 29 poz., rys., tab., wykr.
Twórcy
  • Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Bibliografia
  • [1] A. Jodejko-Pietruczuk, S. Werbińska-Wojciechowska, Analysis of maintenance models' parameters estimation for technical systems with delay time, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (2) (2014) 288–294.
  • [2] K. Antosz, D. Stadnicka, The results of the study concerning the identification of the activities realized in the management of the technical infrastructure in large enterprises, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (1) (2014) 112–119.
  • [3] D. Mazurkiewicz, Problems of numerical simulation of stress and strain in the area of the adhesive-bonded joint of a conveyor belt, Archives of Civil and Mechanical Engineering IX (2) (2009) 75–91.
  • [4] D. Mazurkiewicz, Analysis of the ageing impact on the strength of the adhesive sealed joints of conveyor belts, Journal of Materials Processing Technology 208 (2008) 477–485.
  • [5] G. Fedorko, V. Molnar, M. Dovica, T. Toth, M. Kopas, Analysis of pipe conveyor belt damaged by thermal wear, Engineering Failure Analysis 45 (2014) 41–48.
  • [6] G. Fedorko, V. Molnar, D. Marasova, A. Grincova, et al., Failure analysis of belt conveyor damage caused by the falling material. Part I: Experimental measurements and regression models, Engineering Failure Analysis 36 (2014) 30–38.
  • [7] R. Zimroz, L. Jurdziak, R. Blazej, Novel approaches for processing of multi-channels NDT signals for damage detection in conveyor belts with steel cords, Key Engineering Materials 569/570 (2013) 978–985.
  • [8] G. Fedorko, V. Molnár, J. Živčák, M. Dovica, N. Husáková, Failure analysis of textile rubber conveyor belt damage by dynamic wear, Engineering Failure Analysis 28 (2013) 103–114.
  • [9] A. Grincova, D. Marasova, Experimental research and mathematical modeling as an effective tool of assessing failure of conveyor belts, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (2) (2014) 229–235.
  • [10] H. Komander, M. Hardygóra, M. Bajda, G. Komander, P. Lewandowicz, Assessment methods of conveyor belts impact resistance to the dynamic action of a concentrated load, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (4) (2014) 579–584.
  • [11] D. Mazurkiewicz, Computer-aided maintenance and reliability management systems for conveyor belts, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (3) (2014) 377–382.
  • [12] P. Grzegorzewski, Wspomaganie decyzji w warunkach niepewności. Metody statystyczne dla nieprecyzyjnych danych, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2006.
  • [13] A.M. Kwiatkowska, Systemy wspomagania decyzji, Wydawnictwo Naukowe PWN, Warszawa, 2007.
  • [14] J. Korbicz, J.M. Kościelny, Z. Kowalczuk, W. Cholewa, Diagnostyka procesów. Modele, metody sztucznej inteligencji, zastosowania, WNT, Warszawa, 2002.
  • [15] S. Ablameyko, L. Goras, M. Gori, V. Piuri, Neural Networks for Instrumentation, Measurement and Related Industrial Applications, NATO Science Series, Series III: Computer and System Sciences, vol. 185, IOS Press, Amsterdam, 2003.
  • [16] A.J. Curley, H. Hadavinia, A.J. Kinloch, A.C. Taylor, Predicting the service-life of adhesively-bonded joint, International Journal of Fracture 103 (2000) 41–69.
  • [17] D. Valis, K. Pietrucha-Urbanik, Utilization of diffusion processes and fuzzy logic for vulnerability assessment, Eksploatacja i Niezawodnosc – Maintenance and Reliability 16 (1) (2014) 48–55.
  • [18] O. Maimon, L. Rokach, The Data Mining and Knowledge Discovery Handbook, Springer-Verlag, New York, 2005.
  • [19] A.K. Palit, D. Popovic D, Computational Intelligence in Time Series Forecasting. Theory and Engineering Applications, Springer-Verlag, London, 2005.
  • [20] R.N. Yadav, P.K. Kalra, J. John, Time series prediction with single multiplicative neuron model, Applied Soft Computing 7 (2007) 1157–1163.
  • [21] A. Quaerteroni, F. Saleri, Scientific Computing with MATLAB, Springer-Verlag, Berlin, 2005.
  • [22] P. Lula, Wykorzystanie sztucznej inteligencji w prognozowaniu, (2000) www.statsoft.pl/czytelnia/neuron/ wstepsieci.html.
  • [23] D. Rutkowska, Inteligentne systemy obliczeniowe. Algorytmy genetyczne i sieci neuronowe w systemach rozmytych, Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1997.
  • [24] A. Piegat, Modelowanie i sterowanie rozmyte, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2003.
  • [25] J. Harris, Fuzzy Logic Applications in Engineering Science, Springer, Dordrecht, 2006.
  • [26] K. Leiviska, Industrial Applications of Soft Computing – Paper, Mineral and Metal Processing Industries, Studies in Fuzziness and Soft Computing Nr 71, Springer-Verlag, Berlin, 2002.
  • [27] S. Li, M.A. Elbestawi, Tool condition monitoring in machining by fuzzy neural networks, Journal of Dynamic Systems, Measurement, and Control 118 (4) (1996) 665–672.
  • [28] T.J. Ross, Fuzzy Logic with Engineering Applications, Wiley & Sons Ltd., New York, 2005.
  • [29] D. Mazurkiewicz, A Study of Selected Aspects of Operational Diagnosis of Belt Conveyor, Lublin University of Technology, Lublin, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-34a60fce-110d-42ea-a5f8-ccd532efd48a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.