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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0

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  • Zeki Murat Çınar

    (Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

  • Abubakar Abdussalam Nuhu

    (Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

  • Qasim Zeeshan

    (Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

  • Orhan Korhan

    (Department of Industrial Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

  • Mohammed Asmael

    (Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

  • Babak Safaei

    (Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus via Mersin, Turkey)

Abstract

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Suggested Citation

  • Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8211-:d:423999
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    References listed on IDEAS

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    Cited by:

    1. Krzysztof Lalik & Filip Wątorek, 2021. "Predictive Maintenance Neural Control Algorithm for Defect Detection of the Power Plants Rotating Machines Using Augmented Reality Goggles," Energies, MDPI, vol. 14(22), pages 1-18, November.
    2. Bhaskar Roy & Debabrata Bera & Somya Nigam & S. K. Upadhyay, 2022. "A study of turbine failure pattern: a model optimization using machine learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1761-1770, August.
    3. Goran Otić & Oliver Momčilović & Ljiljana Radovanović & Goran Jovanov & Dragica Radosav & Jasmina Pekez, 2021. "Mathematical Analysis of Criteria for Maintenance of Technical Systems in the Function of Achieving Sustainability," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    4. Justyna Łapińska & Iwona Escher & Joanna Górka & Agata Sudolska & Paweł Brzustewicz, 2021. "Employees’ Trust in Artificial Intelligence in Companies: The Case of Energy and Chemical Industries in Poland," Energies, MDPI, vol. 14(7), pages 1-20, April.
    5. Francesco Polese & Carmen Gallucci & Luca Carrubbo & Rosalia Santulli, 2021. "Predictive Maintenance as a Driver for Corporate Sustainability: Evidence from a Public-Private Co-Financed R&D Project," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    6. Valentina De Simone & Valentina Di Pasquale & Maria Elena Nenni & Salvatore Miranda, 2023. "Sustainable Production Planning and Control in Manufacturing Contexts: A Bibliometric Review," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    7. Anbesh Jamwal & Sushma Kumari & Rajeev Agrawal & Monica Sharma & Ismail Gölgeci, 2024. "Unlocking Circular Economy Through Digital Transformation: the Role of Enabling Factors in SMEs," International Journal of Global Business and Competitiveness, Springer, vol. 19(1), pages 24-36, June.
    8. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    9. Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    10. André Marie Mbakop & Joseph Voufo & Florent Biyeme & Jean Raymond Lucien Meva’a, 2022. "Moving to a Flexible Shop Floor by Analyzing the Information Flow Coming from Levels of Decision on the Shop Floor of Developing Countries Using Artificial Neural Network: Cameroon, Case Study," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(2), pages 255-270, June.
    11. Dayo-Olupona, Oluwatobi & Genc, Bekir & Celik, Turgay & Bada, Samson, 2023. "Adoptable approaches to predictive maintenance in mining industry: An overview," Resources Policy, Elsevier, vol. 86(PA).
    12. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2021. "Designing the data supply chain of a smart construction factory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 41-62, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    13. Shiza Mushtaq & M. M. Manjurul Islam & Muhammad Sohaib, 2021. "Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review," Energies, MDPI, vol. 14(16), pages 1-24, August.
    14. Li-Lun & Liu & Yao-Jen & Su, 2022. "Digital Transformation and Strategic Analysis of Human Resource Value," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(6), pages 1-6.
    15. Nathaphon Boonnam & Tanatpong Udomchaipitak & Supattra Puttinaovarat & Thanapong Chaichana & Veera Boonjing & Jirapond Muangprathub, 2022. "Coral Reef Bleaching under Climate Change: Prediction Modeling and Machine Learning," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
    16. Saud Altaf & Shafiq Ahmad & Mazen Zaindin & Shamsul Huda & Sofia Iqbal & Muhammad Waseem Soomro, 2022. "Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network," Sustainability, MDPI, vol. 14(16), pages 1-29, August.
    17. Henry Ekwaro-Osire & Dennis Bode & Klaus-Dieter Thoben & Jan-Hendrik Ohlendorf, 2022. "Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    18. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    19. Maria Polorecka & Jozef Kubas & Pavel Danihelka & Katarina Petrlova & Katarina Repkova Stofkova & Katarina Buganova, 2021. "Use of Software on Modeling Hazardous Substance Release as a Support Tool for Crisis Management," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    20. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    21. Zeki Murat Çınar & Qasim Zeeshan & Orhan Korhan, 2021. "A Framework for Industry 4.0 Readiness and Maturity of Smart Manufacturing Enterprises: A Case Study," Sustainability, MDPI, vol. 13(12), pages 1-32, June.
    22. Bożena Zwolińska & Jakub Wiercioch, 2022. "Selection of Maintenance Strategies for Machines in a Series-Parallel System," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    23. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    24. Marios Karagiovanidis & Xanthoula Eirini Pantazi & Dimitrios Papamichail & Vassilios Fragos, 2023. "Early Detection of Cavitation in Centrifugal Pumps Using Low-Cost Vibration and Sound Sensors," Agriculture, MDPI, vol. 13(8), pages 1-26, August.
    25. Abdallah Moubayed & Abdallah Shami & Anwer Al-Dulaimi, 2022. "On End-to-End Intelligent Automation of 6G Networks," Future Internet, MDPI, vol. 14(6), pages 1-28, May.

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