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Tytuł artykułu

A fuzzy logic modelling of predictive maintenance in rotating machinery

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The study aims to investigate and assess the application of Fuzzy Logic to construct a predictive maintenance model for rotating machinery. Design/methodology/approach: The research uses a mixed approach, with both quantitative and qualitative approaches, and are four main steps: 1) surveying and analysing existing predictive maintenance techniques; 2) determining appropriate expert assessment criteria for predictive maintenance techniques; 3) vibration analysis by the experts; 4) evaluate the performance of rotating machinery with fuzzy logic. Findings: The result of the study will be used to build a rotating machinery predictive maintenance model, which is very similar to the traditional method. The obtained data showed that the efficiency of the rotating machinery and the vibration level were compliant with the standard ISO 10816-3. Thus, such data can be planned for maintenance to maximize benefit. Research limitations/implications: Future research should optimise the model and add additional modules for automatic data collection. The production monitoring system should help collect data by considering downtime, predicting the functional service life of rotating machinery, etc. Practical implications: The proposed model can be used in small water pumps in order to perform predictive maintenance. The conceptual framework was tested, particularly with rotating machinery. Originality/value: The fuzzy logic model is described as the fuzzy of a process with linguistics for greater clarity.
Rocznik
Strony
15--22
Opis fizyczny
Bibliogr. 19 poz.
Twórcy
  • Department of Advanced Manufacturing Technology, Pathumwan Institute of Technology, Bangkok 10330, Thailand
autor
  • Department of Advanced Manufacturing Technology, Pathumwan Institute of Technology, Bangkok 10330, Thailand
autor
  • Department of Industrial Engineering, Kasem Bundit University, Bangkok 10510, Thailand
  • Department of Mechanical Engineering, Pathumwan Institute of Technology, Bangkok 10330, Thailand
Bibliografia
  • [1] Y. Lv, Q. Zhou, Y. Li, W. Li, A predictive maintenance system for multi-granularity faults based on AdaBelief- BP neural network and fuzzy decision making, Advanced Engineering Informatics 49 (2021) 101318. DOI: https://doi.org/10.1016/j.aei.2021.101318
  • [2] S. Selcuk, Predictive maintenance, its implementation and latest trends, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231/9 (2017) 1670-1679. DOI: https://doi.org/10.1177/0954405415601640
  • [3] W. Hongxia, Y. Xiaohui, Y. Ming, Study on predictive maintenance strategy, Advanced Science and Technology Letters 137 (2016) 52-56. DOI: https://doi.org/10.14257/astl.2016.137.10
  • [4] K.T.P. Nguyen, K. Medjaher, A new dynamic predictive maintenance framework using deep learning for failure prognostics, Reliability Engineering and System Safety 188 (2019) 251-262. DOI: https://doi.org/10.1016/j.ress.2019.03.018
  • [5] M. Baban, C.F. Baban, M.D. Suteu, Maintenance decision-making support for textile machines: a knowledge-based approach using fuzzy logic and vibration monitoring, IEEE Access 7 (2019) 83504- 83514. DOI: https://doi.org/10.1109/ACCESS.2019.2923791
  • [6] M. Cerrada, C. Li, R.V. Sánchez, F. Pacheco, D. Cabrera, J.V. de Oliveira, A fuzzy transition-based approach for fault severity prediction in helical gearboxes, Fuzzy Sets and Systems 337 (2018) 52-73. DOI: https://doi.org/10.1016/j.fss.2016.12.017
  • [7] N. Vafaei, R.A. Ribeiro, L.M. Camarinha-Matos, Fuzzy early warning systems for condition-based maintenance, Computers and Industrial Engineering 128 (2019) 736- 746. DOI: https://doi.org/10.1016/j.cie.2018.12.056
  • [8] C.K. Lee, Y. Lv, K.K.H. Ng, W. Ho, K.L. Choy, Design and application of Internet of things-based warehouse management system for smart logistics, International Journal of Production Research 56/8 (2018) 2753-2768. DOI: https://doi.org/10.1080/00207543.2017.1394592
  • [9] M. Karakose, O. Yaman, Complex fuzzy system based predictive maintenance approach in railways, IEEE Transactions on Industrial Informatics 16/9 (2020) 6023- 6032. DOI: https://doi.org/10.1109/TII.2020.2973231
  • [10] F. Arena, M. Collotta, L. Luca, M. Ruggieri, F.G. Termine, Predictive maintenance in the automotive sector: a literature review, Mathematical and Computational Applications 27/1 (2022) 2. DOI: https://doi.org/10.3390/mca27010002
  • [11] M. Baban, C.F. Baban, B. Moisi, A fuzzy logic-based approach for predictive maintenance of grinding wheels of automated grinding lines, Proceedings of the 23rd International Conference on Methods and Models in Automation and Robotics “MMAR”, Miedzyzdroje, Poland, 2018, 483-486. DOI: https://doi.org/10.1109/MMAR.2018.8486144
  • [12] N.M. Thoppil, V. Vasu, C.S.P. Rao, On the criticality analysis of computer numerical control lathe subsystems for predictive maintenance, Arabian Journal for Science and Engineering 45/7 (2020) 5259-5271. DOI: https://doi.org/10.1007/s13369-020-04397-7
  • [13] P.D.S.L. Alexandrino, G.F. Gomes, S.S. Cunha Jr, A robust optimization for damage detection using multi-objective genetic algorithm, neural network and fuzzy decision making, Inverse Problems in Science and Engineering 28/1 (2020) 21-46. DOI: https://doi.org/10.1080/17415977.2019.1583225
  • [14] W. Zhang, D. Yang, H. Wang, Data-driven methods for predictive maintenance of industrial equipment: A survey, IEEE Systems Journal 13/3 (2019) 2213- 2227. DOI: https://doi.org/10.1109/JSYST.2019.2905565
  • [15] J.J. Costello, G.M. West, S.D. McArthur, Machine learning model for event-based prognostics in gas circulator condition monitoring, IEEE Transactions on Reliability 66/4 (2017) 1048-1057. DOI: https://doi.org/10.1109/TR.2017.2727489
  • [16] A. Shamayleh, M. Awad, J. Farhat, IoT based predictive maintenance management of medical equipment, Journal of Medical Systems 44/4 (2020) 72. DOI: https://doi.org/10.1007/s10916-020-1534-8
  • [17] Aradhana, B. Singh, P. Sihag, Predictive models for estimation of labyrinth weir aeration efficiency, Journal of Achievements in Materials and Manufacturing Engineering 105/1 (2021) 18-32. DOI: https://doi.org/10.5604/01.3001.0014.8742
  • [18] L.A. Dobrzański, Role of materials design in maintenance engineering in the context of industry 4.0 idea, Journal of Achievements in Materials and Manufacturing Engineering 96/1 (2019) 12-49. DOI: https://doi.org/10.5604/01.3001.0013.7932
  • [19] M. Asfar, P. Naveenkumar, V. Naveen Kumar, K. Krishnamurthy, Online vibration suppression in lathe machine, Journal of Achievements in Materials and Manufacturing Engineering 77/2 (2016) 69-74. DOI: https://doi.org/10.5604/17348412.1230100
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0c42270a-1232-4800-8b09-faae4f6c9a8f
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