DOI QR코드

DOI QR Code

A Systematic Review of Predictive Maintenance and Production Scheduling Methodologies with PRISMA Approach

  • Received : 2024.01.05
  • Published : 2024.01.30

Abstract

Predictive maintenance has been considered fundamental in the industrial applications in the last few years. It contributes to improve reliability, availability, and maintainability of the systems and to avoid breakdowns. These breakdowns could potentially lead to system shutdowns and to decrease the production efficiency of the manufacturing plants. The present article aims to study how predictive maintenance could be planed into the production scheduling, through a systematic review of literature. . The review includes the research articles published in international journals indexed in the Scopus database. 165 research articles were included in the search using #predictive maintenance# AND #production scheduling#. Press articles, conference and non-English papers are not considered in this study. After careful evaluation of each study for its purpose and scope, 50 research articles are selected for this review by following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. A benchmarking of predictive maintenance methods was used to understand the parameters that contributed to improve the production scheduling. The results of the comparative analysis highlight that artificial intelligence is a promising tool to anticipate breakdowns. An additional impression of this study is that each equipment has its own parameters that have to be collected, monitored and analyzed.

Keywords

References

  1. F. Zhang, S. Nguyen, Y. Mei, M. Zhang, "Genetic Programming for Production Scheduling: An Evolutionary Learning Approach". Springer Singapore. https://doi.org/10.1007/978-981-16-4859-5 
  2. S. C. Graves, "A Review of Production Scheduling. Operations Research", 29(4), 646-675. 1981. https://doi.org/10.1287/opre.29.4.646 
  3. J. Blazewicz, K. H. Ecker, E. Pesch, G. Schmidt, M. Sterna, J. Weglarz, "Handbook on Scheduling: From Theory to Practice". Springer International Publishing. 2009. https://doi.org/10.1007/978-3-319-99849-7 
  4. Y. Suppiah, T. Bhuvaneswari, P. Shen Yee, N. Wei Yue, C. Mun Horng. "Scheduling Single Machine Problem to Minimize Completion Time". TEM Journal, 552-556. 2022. https://doi.org/10.18421/TEM112-08 
  5. S. Nguyen, M. Zhang, M. Johnston, K.C. Tan, "Automatic programming via iterated local search for dynamic job shop scheduling". IEEE Transactions on Cybernetics, 45(1), 1-14. 2015. https://doi.org/10.1109/TCYB.2014.2317488 
  6. F. Zhang, Y. Mei, S. Nguyen, M. Zhang, "Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling". IEEE Transactions on Cybernetics, 51(4), 1797-1811. 2021. https://doi.org/10.1109/TCYB.2020.3024849 
  7. N. Daneshjo, P. Malega, "Changing of the Maintenance System in the Production Plant with the Application of Predictive Maintenance. TEM Journal", 434-441. 2020. https://doi.org/10.18421/TEM92-03 
  8. T.P. Carvalho, F.A.A.M.N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, S.G.S. Alcala, "A systematic literature review of machine learning methods applied to predictive maintenance". Computers & Industrial Engineering, 137, 106024. 2019. https://doi.org/10.1016/j.cie.2019.106024 
  9. V. Gunnerud, B. Foss, "Oil production optimization-A piecewise linear model, solved with two decomposition strategies". Computers & Chemical Engineering, 34, 1803-1812. 2010. https://doi.org/10.1016/j.compchemeng.2009.10.019 
  10. K. Margellos, P.Goulart, P., & Lygeros, J. (2014). On the Road Between Robust Optimization and the Scenario Approach for Chance Constrained Optimization Problems. Automatic Control, IEEE Transactions On, 59, 2258-2263. https://doi.org/10.1109/TAC.2014.2303232 
  11. E. Lughofer, M. Sayed-Mouchaweh, "Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications". Springer International Publishing. 2019. https://doi.org/10.1007/978-3-030-05645-2 
  12. R.K. Mobley, "An introduction to predictive maintenance (2nd ed)". Butterworth-Heinemann. 2002. 
  13. I. Paprocka, D. Krenczyk, A. Burduk, "The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation". Applied Sciences, 11(1), 171. 2021. https://doi.org/10.3390/app11010171 
  14. S. Zhai, M.G. Kandemir, G. Reinhart, "Predictive maintenance integrated production scheduling by applying deep generative prognostics models: Approach, formulation and solution". Production Engineering, 16(1), 65-88. 2022. https://doi.org/10.1007/s11740-021-01064-0 
  15. B. Kitchenham, "Procedures for Performing Systematic Reviews". Keele, UK, Keele Univ., 33. 2004. 
  16. C. Sohrabi, T, Franchi, G. Mathew, A. Kerwan, M. Nicola, M. Griffin, M. Agha, R. Agha, "PRISMA 2020 statement: What's new and the importance of reporting guidelines". International Journal of Surgery, 88, 105918. 2021. https://doi.org/10.1016/j.ijsu.2021.105918 
  17. M.J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, L Shamseer, J. M. Tetzlaff, E. A. Akl, S. E. Brennan, R. Chou, J. Glanville, J.M. Grimshaw, A. Hrobjartsson, M. M. Lalu, T. Li, E. W. Loder, E. Mayo-Wilson, S. McDonald, D. Moher, "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews". International Journal of Surgery, 88, 105906. 2021. https://doi.org/10.1016/j.ijsu.2021.105906 
  18. P. Tugwell, D. Tovey, "PRISMA 2020. Journal of Clinical Epidemiology", 134, A5-A6. 2021. https://doi.org/10.1016/j.jclinepi.2021.04.008 
  19. B. Gundogan, N. Dowlut, S. Rajmohan, M. R. Borrelli, M. Millip, C. Iosifidis, Y. Z. Udeaja, G. Mathew, A. Fowler, R. Agha, "Assessing the compliance of systematic review articles published in leading dermatology journals with the PRISMA statement guidelines: A systematic review". JAAD International, 1(2), 157-174. 2020. https://doi.org/10.1016/j.jdin.2020.07.007 
  20. H. M. Hashemian, W. C. Bean, "State-of-the-Art Predictive Maintenance Techniques". IEEE Transactions on Instrumentation and Measurement, 60(10), 3480-3492. 2011. https://doi.org/10.1109/TIM.2009.2036347 
  21. T. Kufner, F. Dopper, D. Muller, A. G. Trenz, "Predictive Maintenance: Using Recurrent Neural Networks for Wear Prognosis in Current Signatures of Production Plants". International Journal of Mechanical Engineering and Robotics Research, 583-591. 2021. https://doi.org/10.18178/ijmerr.10.11.583-591 
  22. I. Paprocka, W. M. Kempa, "Model of Production System Evaluation with the Influence of FDM Machine Reliability and Process-Dependent Product Quality". Materials, 14(19), 5806. 2021. https://doi.org/10.3390/ma14195806 
  23. E. Arena, A. Corsini, R. Ferulano, D. A. Iuvara, E. S. Miele, L. Ricciardi Celsi, N. A. Sulieman, M. Villari, "Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis". Energies, 14(13), 3951. 2021. https://doi.org/10.3390/en14133951 
  24. I.Aslanidou, M. Rahman, V. Zaccaria, K. G. Kyprianidis, "Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements". Frontiers in Mechanical Engineering, 7, 51. 2021. https://doi.org/10.3389/fmech.2021.676853 
  25. A. Ladj, F. B.-S. Tayeb, C. Varnier, "Hybrid of metaheuristic approaches and fuzzy logic for the integrated flowshop scheduling with predictive maintenance problem under uncertainties". European Journal of Industrial Engineering. 2021. https://www.inderscienceonline.com/doi/abs/10.1504/EJIE.2021.117325 
  26. Eppinger Thomas, Longwell Glenn, Mas Peter, Goodheart Kevin, Badiali Umberto, & Aglave Ravindra, "Increase Food Production Efficiency Using the Executable Digital Twin (xdt)". Chemical Engineering Transactions, 87, 37-42. 2021. https://doi.org/10.3303/CET2187007 
  27. I. Paprocka, W. M. Kempa, G. Cwikla, "Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution". Sensors, 20(23), 6787. 2020. https://doi.org/10.3390/s20236787 
  28. A. Essien, C. Giannetti, "A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders". IEEE Transactions on Industrial Informatics, 16(9), 6069-6078. 2020. https://doi.org/10.1109/TII.2020.2967556 
  29. C. Morariu, O. Morariu, S. Raileanu, T. Borangiu, "Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems". Computers in Industry, 120, 103244. 2020. https://doi.org/10.1016/j.compind.2020.103244 
  30. N. Daneshjo, P. Malega, "Changing of the Maintenance System in the Production Plant with the Application of Predictive Maintenance". TEM Journal, 434-441. 2020. https://doi.org/10.18421/TEM92-03 
  31. M. Ghaleb, S. Taghipour, M. Sharifi, H. Zolfagharinia, "Integrated production and maintenance scheduling for a single degrading machine with deterioration-based failures". Computers & Industrial Engineering, 143, 106432. 2020. https://doi.org/10.1016/j.cie.2020.106432 
  32. F. C. M. Thom, J. R. B. Zoghbi, M. S. da R. Freitas, G. R. Sisquini, "Dynamic risk calculation model applied to gas compressor". REM - International Engineering Journal, 73, 33-41. 2019. https://doi.org/10.1590/0370-44672018730192 
  33. G. Herranz, A. Antolinez, J. Escartin, A. Arregi, J. Gerrikagoitia, "Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis". Journal of Manufacturing and Materials Processing, 3(4), 97. 2019. https://doi.org/10.3390/jmmp3040097 
  34. S. Antomarioni, O. Pisacane, D. Potena, M. Bevilacqua, F. E. Ciarapica, C. Diamantini, "A predictive association rule-based maintenance policy to minimize the probability of breakages: Application to an oil refinery". The International Journal of Advanced Manufacturing Technology, 105(9), 3661-3675. 2019. https://doi.org/10.1007/s00170-019-03822-y 
  35. Q. Liu, M. Dong, F. F. Chen, W. Lv, C. Ye, "Single-machine-based joint optimization of predictive maintenance planning and production scheduling". Robotics and Computer-Integrated Manufacturing, 55, 173-182. 2019. https://doi.org/10.1016/j.rcim.2018.09.007 
  36. D. F. Hesser, B. Markert, "Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks". Manufacturing Letters, 19, 1-4. 2019. https://doi.org/10.1016/j.mfglet.2018.11.001 
  37. S. S. Baliarsingh, "Wear particle analysis of an antifriction bearing". International Journal of Mechanical Engineering and Technology, 9(3), 684-699.
  38. Q. Qi, F. Tao, "Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison". IEEE Access, 6, 3585-3593. 2018. https://doi.org/10.1109/ACCESS.2018.2793265 
  39. H. Rodseth, P. Schjolberg, A. Marhaug, "Deep digital maintenance". Advances in Manufacturing, 5(4), 299-310. 2017. https://doi.org/10.1007/s40436-017-0202-9 
  40. H. Peng, G.-J. van Houtum, "Joint optimization of condition-based maintenance and production lot-sizing". European Journal of Operational Research, 253(1), 94-107. 2016. https://doi.org/10.1016/j.ejor.2016.02.027 
  41. L. Waltersmann, S. Kiemel, J. Stuhlsatz, A. Sauer, R. Miehe, "Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies-A Comprehensive Review". Sustainability, 13(12), 6689. 2021. https://doi.org/10.3390/su13126689 
  42. S. Dutta, N. S. K. Reddy, "Adaptive and noncyclic preventive maintenance to augment production activities". Journal of Quality in Maintenance Engineering, 27(1), 92-106. 2020. https://doi.org/10.1108/JQME-03-2018-0017 
  43. S. Martins, M. L. R. Varela, J. Machado, "Development of a System for Supporting Industrial Management". In V. Ivanov, J. Trojanowska, J. Machado, O. Liaposhchenko, J. Zajac, I. Pavlenko, M. Edl, & D. Perakovic (Eds.), Advances in Design, Simulation and Manufacturing II (pp. 209-215). Springer International Publishing. 2020. https://doi.org/10.1007/978-3-030-22365-6_21 
  44. H. Yihai, G. Changchao, H. Xiao, C. Jiaming, C. Zhaoxiang, "Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network". 2017. https://journals.sagepub.com/doi/10.1177/1748006X17728599 
  45. N. Do, "Integration of design and manufacturing data to support personal manufacturing based on 3D printing services". The International Journal of Advanced Manufacturing Technology, 90(9), 3761-3773. 2017. https://doi.org/10.1007/s00170-016-9688-8 
  46. A. Legarretaetxebarria, M. Quartulli, I. Olaizola, M. Serrano, "Optimal scheduling of manufacturing processes across multiple production lines by polynomial optimization and bagged bounded binary knapsack". International Journal on Interactive Design and Manufacturing (IJIDeM), 11(1), 83-91. 2017. https://doi.org/10.1007/s12008-016-0323-6 
  47. P. Ershun, L. Wenzhu, X. Lifeng, "A joint model of production scheduling and predictive maintenance for minimizing job tardiness". SpringerLink. Retrieved October 31, 2021, from https://link.springer.com/article/10.1007%2Fs00170-011-3652-4 
  48. J.-Y. Shiau, "Effectivity date analysis and scheduling". International Journal of Production Research, 49(10), 2771-2791. 2011. https://doi.org/10.1080/00207541003713017 
  49. T. Schlegel, S. Thiel, M. Foursa, F. Meo, J. Larranaga, J. A. Ibarbia, G. Haidegger, I. Mezgar, I. Paniti, A. H. Praturlon, J. Canou, "Smart connected and interactive production control in a distributed environment". International Journal of Computer Aided Engineering and Technology, 3(3/4), 322. 2011. https://doi.org/10.1504/IJCAET.2011.040051 
  50. H. Dehghan Shoorkand, M. Nourelfath, and A. Hajji, "A deep learning approach for integrated production planning and predictive maintenance," International Journal of Production Research, vol. 61, no. 23, pp. 7972-7991, Dec. 2023, doi: 10.1080/00207543.2022.2162618 
  51. M. Arena, V. Di Pasquale, R. Iannone, S. Miranda, and S. Riemma, "A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule," Adv. Manuf., vol. 10, no. 2, pp. 205-219, Jun. 2022, doi: 10.1007/s40436-021-00380-z. 
  52. T. Zonta, C. A. da Costa, F. A. Zeiser, G. de Oliveira Ramos, R. Kunst, and R. da Rosa Righi, "A predictive maintenance model for optimizing production schedule using deep neural networks," Journal of Manufacturing Systems, vol. 62, pp. 450-462, Jan. 2022, doi: 10.1016/j.jmsy.2021.12.013. 
  53. A. Salmasnia and S. Dehghani, "A production-inventory model under quality-maintenance policy with rework process in the presence of random failures and multiple assignable causes," International Journal of Modelling and Simulation, vol. 43, no. 6, pp. 832-848, Nov. 2023, doi: 10.1080/02286203.2022.2127053. 
  54. G. Bencheikh, A. Letouzey, and X. Desforges, "An approach for joint scheduling of production and predictive maintenance activities," Journal of Manufacturing Systems, vol. 64, pp. 546-560, Jul. 2022, doi: 10.1016/j.jmsy.2022.08.005. 
  55. V. K, S. S, V. P, S. R, and G. Di Bona, "Availability Analysis of the Critical Production System in SMEs Using the Markov Decision Model," Mathematical Problems in Engineering, vol. 2022, p. e6026984, Sep. 2022, doi: 10.1155/2022/6026984. 
  56. T. Xia et al., "Collaborative production and predictive maintenance scheduling for flexible flow shop with stochastic interruptions and monitoring data," Journal of Manufacturing Systems, vol. 65, pp. 640-652, Oct. 2022, doi: 10.1016/j.jmsy.2022.10.016. 
  57. J. Wodecki, P. Krot, A. Wroblewski, K. Chudy, and R. Zimroz, "Condition Monitoring of Horizontal Sieving Screens-A Case Study of Inertial Vibrator Bearing Failure in Calcium Carbonate Production Plant," Materials, vol. 16, no. 4, Art. no. 4, Jan. 2023, doi: 10.3390/ma16041533. 
  58. X. Li, D. Chang, and Y. Sun, "Data-driven predictive maintenance method for digital welding machines," Materia (Rio J.), vol. 28, p. e20230096, May 2023, doi: 10.1590/1517-7076-RMAT-2023-0096. 
  59. K. S. H. Ong, W. Wang, D. Niyato, and T. Friedrichs, "Deep-Reinforcement-Learning-Based Predictive Maintenance Model for Effective Resource Management in Industrial IoT," IEEE Internet of Things Journal, vol. 9, no. 7, pp. 5173-5188, Apr. 2022, doi: 10.1109/JIOT.2021.3109955. 
  60. H. Zermane and A. Drardja, "Development of an efficient cement production monitoring system based on the improved random forest algorithm," Int J Adv Manuf Technol, vol. 120, no. 3, pp. 1853-1866, May 2022, doi: 10.1007/s00170-022-08884-z. 
  61. L. Romano, M. Godio, P. Johannesson, F. Bruzelius, T. Ghandriz, and B. Jacobson, "Development of the Vastra Gotaland Operating Cycle for Long-Haul Heavy-Duty Vehicles," IEEE Access, vol. 11, pp. 73268-73302, 2023, doi: 10.1109/ACCESS.2023.3295989. 
  62. H. Gao, Y. Li, Y. Zhao, and Y. Song, "Dual Channel Feature Attention-Based Approach for RUL Prediction Considering the Spatiotemporal Difference of Multisensor Data," IEEE Sensors Journal, vol. 23, no. 8, pp. 8514-8525, Apr. 2023, doi: 10.1109/JSEN.2023.3246595. 
  63. A. Bagozi, D. Bianchini, and A. Rula, "Multi-perspective Data Modelling in Cyber Physical Production Networks: Data, Services and Actors," Data Sci. Eng., vol. 7, no. 3, pp. 193-212, Sep. 2022, doi: 10.1007/s41019-022-00194-4. 
  64. Y. Shin et al., "Multiple linear regression and GRU model for the online prediction of catalyst activity and lifetime in counter-current continuous catalytic reforming," Korean J. Chem. Eng., vol. 40, no. 6, pp. 1284-1296, Jun. 2023, doi: 10.1007/s11814-023-1378-2. 
  65. L.-C. Kung and Z.-Y. Liao, "Optimization for a Joint Predictive Maintenance and Job Scheduling Problem With Endogenous Yield Rates," IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1555-1566, Jul. 2022, doi: 10.1109/TASE.2022.3173822. 
  66. S. Zhai, M. G. Kandemir, and G. Reinhart, "Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution," Prod. Eng. Res. Devel., vol. 16, no. 1, pp. 65-88, Feb. 2022, doi: 10.1007/s11740-021-01064-0. 
  67. M. M. Hamasha et al., "Strategical selection of maintenance type under different conditions," Sci Rep, vol. 13, no. 1, Art. no. 1, Sep. 2023, doi: 10.1038/s41598-023-42751-5.