journal article·2022-08-12Open access

On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges

Mounia Achouch, Mariya Dimitrova, Khaled Ziane, Sasan Sattarpanah Karganroudi, Rizck Dhouib, Hussein Ibrahim, Mehdi Adda

Applied Sciences· Volume 12· Issue 16

Abstract

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Quality and Safety in Healthcare · Reliability and Maintenance Optimization · Digital Transformation in Industry

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institut Technologique de Maintenance Industrielle ITMI, 175 rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada

Département de Mathématique, Informatique et Génie, Université du Québec à Rimouski, Rimouski, QC G56 3A1, Canada

Centre de Recherche et D’innovation en Intelligence Énergétique CR2Ie, 175 rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada

Department of Mechanical Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada

References (87)

Show source-provided references
1. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M. (2015). Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, Boston Consulting Group.
2. Maintenance 4.0 Technologies for Sustainable Manufacturing—An Overview10.1016/j.ifacol.2019.10.005 ↗
3. Ran, Y., Zhou, X., Lin, P., Wen, Y., and Deng, R. (2019). A Survey of Predictive Maintenance: Systems, Purposes and Approaches. arXiv, Available online: http://arxiv.org/abs/1912.07383.
4. Exploring predictive maintenance applications in industry10.1108/JQME-05-2020-0029 ↗
5. Blancke, O. (2020). Développement D’une Approche de Pronostic Pour les Équipements Complexes Permettant L’application de la Maintenance Prévisionnelle. [Ph.D. Thesis, École de Technologie Supérieure].
6. (2022, July 24). Market Research Report, Marketsandmarkets.com. “Predictive Maintenance Market by Component (Solutions, Services), De-ployment Mode (On-Premises, Cloud), Organization Size (Large Enterprises, SME), Vertical and Region—Global Forecast to 2026”. Available online: https://www.grandviewresearch.com/industry-analysis/predictive-maintenance-market.
7. (2022, July 24). Predictive Maintenance Market Size Worldwide 2020–2030|Statista. Available online: https://www.statista.com/statistics/748080/global-predictive-maintenance-market-size/.
8. (2022, April 13). Qu’est ce Que L’industrie 4.0? L’agence Digitale Créative—Les Digital Heroes. Available online: https://lesdigitalheroes.fr/quest-ce-que-lindustrie-4-0/.
9. Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario10.1007/s40436-017-0203-8 ↗
10. Maintenance transformation through Industry 4.0 technologies: A systematic literature review10.1016/j.compind.2020.103335 ↗
11. A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.010.1109/ACCESS.2018.2833501 ↗
12. Industry 4.0—A Glimpse10.1016/j.promfg.2018.02.034 ↗
14. Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities10.1007/s10462-020-09942-2 ↗
15. Security challenges of the Industry 4.010.1088/1742-6596/1515/3/032074 ↗
16. Network and information security challenges within Industry 4.0 paradigm10.1016/j.promfg.2017.09.047 ↗
17. Kashmar, N., Adda, M., and Atieh, M. (2019, January 14–15). From access control models to access control metamodels: A survey. Proceedings of the Future of Information and Communication Conference, San Francisco, CA, USA.10.1007/978-3-030-12385-7_61 ↗
18. Pedreira, V., Barros, D., and Pinto, P. (2021). A review of attacks, vulnerabilities, and defenses in industry 4.0 with new chal-lenges on data sovereignty ahead. Sensors, 21.10.3390/s21155189 ↗
19. Human factors and ergonomics in manufac-turing in the industry 4.0 context–A scoping review10.1016/j.techsoc.2021.101572 ↗
20. Industry 4.0 and the human factor–A systems frame-work and analysis methodology for successful development10.1016/j.ijpe.2020.107992 ↗
21. Lessons learned from Industry 4.0 implementation in the German manufacturing industry10.1108/JMTM-08-2018-0270 ↗
22. Pilloni, V. (2018). How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0. Future Internet, 10.10.3390/fi10030024 ↗
23. Intelligent Maintenance Systems and Predictive Manufacturing10.1115/1.4047856 ↗
24. A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis10.1016/j.rser.2015.02.052 ↗
25. Levitt, J. (2003). Complete Guide to Preventive and Predictive Maintenance, Industrial Press Inc.
26. Pech, M., Vrchota, J., and Bednář, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors, 21.10.3390/s21041470 ↗
27. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system10.1016/j.cie.2020.106948 ↗
28. Poór, P., Basl, J., and Zenisek, D. (2019, January 28). Predictive Maintenance 4.0 as next evolution step in industrial maintenance development. Proceedings of the 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka.10.23919/SCSE.2019.8842659 ↗
29. Jimenez-Cortadi, A., Irigoien, I., Boto, F., Sierra, B., and Rodriguez, G. (2019). Predictive Maintenance on the Machining Process and Machine Tool. Appl. Sci., 10.10.3390/app10010224 ↗
30. Maktoubian, J., Taskhiri, M.S., and Turner, P. (2021). Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities. Forests, 12.10.3390/f12111495 ↗
31. Okano, M.T. (2017, January 25–26). IOT and Industry 4.0: The Industrial New Revolution. Proceedings of the ICMIS-17—International Conference on Management and Information Systems, Bangkok, Thailand.
32. Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation10.1016/j.procir.2015.08.026 ↗
33. Using Industry 4.0 to face the challenges of Predictive Mainte-nance: A Key Performance Indicators development in a Cyber Physical System10.1016/j.cie.2022.108400 ↗
34. Big data analytics for intelligent manufacturing systems: A review10.1016/j.jmsy.2021.03.005 ↗
35. Kaarlela, T., Arnarson, H., Pitkäaho, T., Shu, B., Solvang, B., and Pieskä, S. (2022). Common Educational Teleoperation Plat-form for Robotics Utilizing Digital Twins. Machines, 10.10.3390/machines10070577 ↗
36. Azeez, N.A., and Adjekpiyede, O.O. (2022). Digital Twin Technology: A Review of Its Applications and Prominent Challenges. Covenant J. Inform. Commun. Technol., 10, Available online: https://journals.covenantuniversity.edu.ng/index.php/cjict/article/view/2995.
37. A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics10.1016/j.enbuild.2022.111988 ↗
38. Advances of Digital Twins for Predictive Maintenance10.1016/j.procs.2022.01.348 ↗
39. Wang, Y., Martinsen, K., Yu, T., and Wang, K. (2022). Advanced Manufacturing and Automation XI, Springer.10.1007/978-981-19-0572-8 ↗
40. Augmented reality in vocational training: A systematic review of research and applications10.1016/j.chb.2021.107125 ↗
41. Ho, P.T., Albajez, J.A., Santolaria, J., and Yagüe-Fabra, J.A. (2022). Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review. Appl. Sci., 12.10.3390/app12041961 ↗
42. Arena, F., Collotta, M., Pau, G., and Termine, F. (2022). An Overview of Augmented Reality. Computers, 11.10.3390/computers11020028 ↗
43. Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing10.1016/j.rcim.2022.102357 ↗
44. Artificial intelligence for predictive maintenance in the railcar learning factories10.1016/j.promfg.2020.04.032 ↗
45. A systematic literature review of machine learning methods applied to predictive maintenance10.1016/j.cie.2019.106024 ↗
46. Artificial intelligence techniques for enabling Big Data services in distribution networks: A review10.1016/j.rser.2021.111459 ↗
47. Nacchia, M., Fruggiero, F., Lambiase, A., and Bruton, K. (2021). A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector. Appl. Sci., 11.10.3390/app11062546 ↗
48. zu Wickern, V.F.M. (2019). Challenges and Reliability of Predictive Maintenance. [Master’s Thesis, Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences].
49. Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants10.1016/j.arcontrol.2022.04.001 ↗
50. Diamond, S. (2013). Predictive Maintenance For Dummies®, IBM Limited Edition, John Wiley & Sons, Inc.
51. Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing
52. Keleko, A.T., Kamsu-Foguem, B., Ngouna, R.H., and Tongne, A. (2022). Artificial intelligence and real-time predictive maintenance in industry 4.0: A bibliometric analysis. AI Ethics, 1–25.10.1007/s43681-021-00132-6 ↗
53. Mousavi, S. (2021). Simultaneous Control of the Production, Maintenance, and Inspection Strategies for a Failure-Prone Manufacturing System with Quality-Based Financial Penalties/Incentives. [Ph.D. Thesis, École de Technologie Supérieure].
54. Paleyes, A., Urma, R.G., and Lawrence, N.D. (2020). Challenges in deploying machine learning: A survey of case studies. ACM Comput. Surv. (CSUR).
55. Sipos, R., Fradkin, D., Moerchen, F., and Wang, Z. (2014, January 24–27). Log-based predictive maintenance. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.10.1145/2623330.2623340 ↗
56. Ton, B., Basten, R., Bolte, J., Braaksma, J., Di Bucchianico, A., Van De Calseyde, P., Grooteman, F., Heskes, T., Jansen, N., and Teeuw, W. (2020). PrimaVera: Synergising Predictive Maintenance. Appl. Sci., 10.10.3390/app10238348 ↗
57. CBM, TPM, RCM and A-RCM-a qualitative comparison of maintenance management strategies
58. Fernandes, J., Reis, J., Melão, N., Teixeira, L., and Amorim, M. (2021). The role of Industry 4.0 and BPMN in the arise of condition-based and predictive maintenance: A case study in the automotive industry. Appl. Sci., 11.10.3390/app11083438 ↗
59. Ogbonnaya, C., Abeykoon, C., Nasser, A., and Turan, A. (2021). A computational approach to solve a system of transcendental equations with multi-functions and multi-variables. Mathematics, 9.10.3390/math9090920 ↗
60. Stratégie pour la maintenance prévisionnelle des systèmes photovoltaïques
61. Current status of machine prognostics in condition-based maintenance: A review10.1007/s00170-009-2482-0 ↗
62. Bengtsson, M. (2007). On Condition Based Maintenance and Its Implementation in Industrial Settings. [Ph.D. Thesis, Mälardalens Högskola].
63. Hess, A., Calvello, G., and Dabney, T. (2004, January 6–13). PHM a key enabler for the JSF autonomic logistics support concept. Proceedings of the 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. no. 04th8720), Big Sky, MT, USA.10.1109/AERO.2004.1368171 ↗
64. A review on prognostics and health management (PHM) methods of lithium-ion batteries10.1016/j.rser.2019.109405 ↗
65. Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications10.1016/j.compind.2021.103414 ↗
66. Hu, N., Su, X., and Liu, B. (2018, January 27–29). A Review on Prognostics and Health Management. Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018), Xi’an, China.
67. Sanchez, R.E.S. (2016). Contribution au Pronostic de Durée de vie des Systèmes Pile à Combustible de Type PEMFC. [Ph.D. Thesis, Université du Québec à Trois-Rivières].
68. An Adaptive Maintenance Policy With Nonlinear Degradation Modeling Based on Prognostic Information10.1109/ACCESS.2020.3020375 ↗
69. Review of Key Technologies and Progress in Industrial Equipment Health Management10.1109/ACCESS.2020.3017626 ↗
70. A review of diagnostic and prognostic capabilities and best practices for manufacturing10.1007/s10845-016-1228-8 ↗
71. Modeling Approaches for Prognostics and Health Management of Electronics
72. Remaining useful life estimation—A review on the statistical data driven approaches10.1016/j.ejor.2010.11.018 ↗
73. Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics10.1016/j.ress.2022.108341 ↗
74. Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction10.1109/ACCESS.2020.3010066 ↗
75. Predictive maintenance in the Industry 4.0: A systematic literature review10.1016/j.cie.2020.106889 ↗
76. A Framework for Predicting Remaining Useful Life Curve of Rolling Bearings under Defect Progression Based on Neural Network and Bayesian Method10.1109/ACCESS.2021.3073945 ↗
77. Using Long-Short term Memory networks with Genetic Algorithm to predict engine condition
78. Thakkar, U.R. (2021). Remaining Useful Life Prediction of a Turbofan Engine Using Deep Layer Recurrent Neural Networks. [Ph.D. Thesis, Carleton University].10.3390/act11030067 ↗
79. Performance improvement of centrifugal compressors for fuel cell vehicles using the aerodynamic optimization and data mining methods10.1016/j.ijhydene.2020.02.026 ↗
80. (2022, April 20). La maintenance Prédictive Intelligente Pour L’industrie 4.0. Techniques de L’ingénieur. Available online: https://www.techniques-ingenieur.fr/base-documentaire/genie-industriel-th6/mise-en-uvre-de-la-maintenance-42136210/la-maintenance-predictive-intelligente-pour-l-industrie-4-0-mt9572/.
81. Nordal, H., and El-Thalji, I. (2021). Assessing the Technical Specifications of Predictive Maintenance: A Case Study of Centrifugal Compressor. Appl. Sci., 11.10.3390/app11041527 ↗
82. Ali, J. (2020). Assessing the Impact of Business Model for Predictive Maintenance Scenarios Using Multimethod Simulation: A Case Study of Centrifugal Compressor. [Master’s Thesis, University of Stavanger].
83. Gholami, H., Abu, F., Lee, J.K.Y., Karganroudi, S.S., and Sharif, S. (2021). Sustainable Manufacturing 4.0—Pathways and Practices. Sustainability, 13.10.3390/su132413956 ↗
84. Aminzadeh, A., Sattarpanah Karganroudi, S., Meiabadi, M.S., Mohan, D.G., and Ba, K. (2022). A Survey of Process Monitor-ing Using Computer-Aided Inspection in Laser-Welded Blanks of Light Metals Based on the Digital Twins Concept. Quantum Beam Sci., 6.10.3390/qubs6020019 ↗
85. DMAIC-based approach to sus-tainable value stream mapping: Towards a sustainable manufacturing system10.1080/1331677X.2020.1715236 ↗
86. Sustainability-oriented Application of Value Stream Mapping: A review and classification10.1109/ACCESS.2021.3077570 ↗
87. (2022, July 28). Flexible Data Communication and IIoT Platform in Industrial Applications—Ewon Flexy. Available online: www.ewon.biz.