Predictive Maintenance Strategies for Industry 4.0: A Modelling Approach
DOI:
https://doi.org/10.63876/ijtm.v3i3.121Keywords:
Predictive Maintenance, Industry 4.0, Modelling Approach, Internet of Things (IoT), Machine Learning, Smart ManufacturingAbstract
The advent of Industry 4.0 has revolutionized industrial operations by integrating advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics into manufacturing systems. Among its many applications, predictive maintenance emerges as a critical strategy to minimize downtime, reduce operational costs, and enhance asset longevity. This article presents a modelling approach to predictive maintenance tailored for Industry 4.0 environments. We explore how real-time data acquisition and machine learning algorithms can be integrated into a predictive maintenance framework, enabling early fault detection and optimal scheduling of maintenance activities. The study proposes a comprehensive model that incorporates sensor data analysis, failure prediction, and decision support systems. Simulations and case studies demonstrate the effectiveness of the proposed approach in increasing system reliability and efficiency. Our findings highlight the pivotal role of data-driven models in transforming traditional maintenance practices into proactive, intelligent maintenance strategies suitable for smart factories.
Downloads
References
M. M. Hossain and G. Purdy, “Role of Industry 4.0 in zero-defect manufacturing: A systematic literature review and a conceptual framework for future research directions,” Manuf. Lett., vol. 41, pp. 1696–1707, Oct. 2024, doi: https://doi.org/10.1016/j.mfglet.2024.09.197.
M. L. Chaves Franz, N. F. Ayala, and A. M. Larranaga, “Industry 4.0 for passenger railway companies: A maturity model proposal for technology management,” J. Rail Transp. Plan. Manag., vol. 32, p. 100480, Dec. 2024, doi: https://doi.org/10.1016/j.jrtpm.2024.100480.
D. K. Agrawal, S. K. Udgata, and W. Usaha, “Leveraging Smartphone Sensor Data and Machine Learning Model for Human Activity Recognition and Fall Classification,” Procedia Comput. Sci., vol. 235, pp. 1980–1989, 2024, doi: https://doi.org/10.1016/j.procs.2024.04.187.
N. Es-sakali, M. Cherkaoui, M. O. Mghazli, and Z. Naimi, “Review of predictive maintenance algorithms applied to HVAC systems,” Energy Reports, vol. 8, pp. 1003–1012, Nov. 2022, doi: https://doi.org/10.1016/j.egyr.2022.07.130.
R. van Dinter, B. Tekinerdogan, and C. Catal, “Predictive maintenance using digital twins: A systematic literature review,” Inf. Softw. Technol., vol. 151, p. 107008, Nov. 2022, doi: https://doi.org/10.1016/j.infsof.2022.107008.
D. Sanchez-Londono, I. Roda, and G. Barbieri, “The requirements of a value model for the strategic implementation of predictive maintenance,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 1276–1281, 2023, doi: https://doi.org/10.1016/j.ifacol.2023.10.1759.
O. Dayo-Olupona, B. Genc, T. Celik, and S. Bada, “Adoptable approaches to predictive maintenance in mining industry: An overview,” Resour. Policy, vol. 86, p. 104291, Oct. 2023, doi: https://doi.org/10.1016/j.resourpol.2023.104291.
N. Kosaka et al., “Decision-making support utilizing real-time tsunami inundation and damage forecast,” Int. J. Disaster Risk Reduct., vol. 94, p. 103807, Aug. 2023, doi: https://doi.org/10.1016/j.ijdrr.2023.103807.
A. Beckers et al., “Digitalized manufacturing process sequences – foundations and analysis of the economic and ecological potential,” CIRP J. Manuf. Sci. Technol., vol. 39, pp. 387–400, Nov. 2022, doi: https://doi.org/10.1016/j.cirpj.2022.09.001.
S. Aburakhia and A. Shami, “SB-PdM: A tool for predictive maintenance of rolling bearings based on limited labeled data,” Softw. Impacts, vol. 16, p. 100503, May 2023, doi: https://doi.org/10.1016/j.simpa.2023.100503.
C. Chen, H. Fu, Y. Zheng, F. Tao, and Y. Liu, “The advance of digital twin for predictive maintenance: The role and function of machine learning,” J. Manuf. Syst., vol. 71, pp. 581–594, Dec. 2023, doi: https://doi.org/10.1016/j.jmsy.2023.10.010.
X. Wang, M. Liu, C. Liu, L. Ling, and X. Zhang, “Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing,” Expert Syst. Appl., vol. 234, p. 121136, Dec. 2023, doi: https://doi.org/10.1016/j.eswa.2023.121136.
M. Li, X. Jiang, J. Carroll, and R. R. Negenborn, “A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty,” Appl. Energy, vol. 321, p. 119284, Sep. 2022, doi: https://doi.org/10.1016/j.apenergy.2022.119284.
J. Zenisek, F. Holzinger, and M. Affenzeller, “Machine learning based concept drift detection for predictive maintenance,” Comput. Ind. Eng., vol. 137, p. 106031, Nov. 2019, doi: https://doi.org/10.1016/j.cie.2019.106031.
Y. Zhang, F. Shen, M. Li, and C. Wu, “Predicting for I/O stack optimizations on cyber–physical systems,” Microprocess. Microsyst., vol. 101, p. 104896, Sep. 2023, doi: https://doi.org/10.1016/j.micpro.2023.104896.
C. Fan, X. Lai, H. Wen, and L. Yang, “Coal and gas outburst prediction model based on principal component analysis and improved support vector machine,” Geohazard Mech., vol. 1, no. 4, pp. 319–324, Dec. 2023, doi: https://doi.org/10.1016/j.ghm.2023.11.003.
M. Jiang, J. Wang, L. Hu, and Z. He, “Random forest clustering for discrete sequences,” Pattern Recognit. Lett., vol. 174, pp. 145–151, Oct. 2023, doi: https://doi.org/10.1016/j.patrec.2023.09.001.
K. Xu, A. M. Tartakovsky, J. Burghardt, and E. Darve, “Learning viscoelasticity models from indirect data using deep neural networks,” Comput. Methods Appl. Mech. Eng., vol. 387, p. 114124, Dec. 2021, doi: https://doi.org/10.1016/j.cma.2021.114124.
Z. Zhang, W. Zhang, K. Yang, and S. Zhang, “Remaining useful life prediction of lithium-ion batteries based on attention mechanism and bidirectional long short-term memory network,” Measurement, vol. 204, p. 112093, Nov. 2022, doi: https://doi.org/10.1016/j.measurement.2022.112093.
Z. Gao et al., “Conformal PDMS films for strengthening flexibility and retaining absorption in pyramid ultra-thin c-Si solar cells,” Sol. Energy, vol. 247, pp. 520–530, Nov. 2022, doi: https://doi.org/10.1016/j.solener.2022.10.018.
S. Yan, H. Shao, Z. Min, J. Peng, B. Cai, and B. Liu, “FGDAE: A new machinery anomaly detection method towards complex operating conditions,” Reliab. Eng. Syst. Saf., vol. 236, p. 109319, Aug. 2023, doi: https://doi.org/10.1016/j.ress.2023.109319.
