Skip to main content
SpringerLink
Log in

Real-Time Predictive Maintenance-Based Process Parameters: Towards an Industrial Sustainability Improvement

  • Conference paper
  • First Online:
International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023) (AI2SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 931))

  • 176 Accesses

Abstract

Real-time predictive maintenance is a pivotal driver for the evolution of smart factories, deeply impacting the realm of sustainable industry practices. Its core function of continuous monitoring and analysis of manufacturing process parameters not only ensures operational efficiency but also significantly contributes to sustainable practices. By promptly identifying deviations from standard operating conditions and issuing early alerts for potential issues, this approach plays a vital role in extending the lifespan of machinery and optimizing resource-intensive maintenance activities.

This paper extensively explores the proactive decision-making facet of real-time predictive maintenance, necessitating the seamless integration of sensor technologies, data acquisition systems, and advanced analytics platforms. The study places particular emphasis on the application of online learning algorithms to construct a robust prediction model that delves into the correlation between changing process parameters and degradation factors, offering a comprehensive insight into machine behavior.

This paper is structured as follows: We begin with a comprehensive introduction that highlights the increasing significance of predictive maintenance in smart factories and its profound implications for sustainable industry practices. Then, the paper delves into the challenges surrounding proactive decision-making in real-time predictive maintenance. The crux of the problematic landscape is the need to construct a robust prediction model, combined with the complexity of analyzing features in a dynamic environment. The Methodology section employs a rigorous review methodology to analyze and synthesize the existing body of knowledge in real-time predictive maintenance. The core of this review focuses on synthesizing results that contribute to perspectives on sustainability improvement. The culmination of our review is the conclusion, which encapsulates the paper’s overarching findings.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adam, J., Barszcz, T.: Vulnerabilities and fruits of smart monitoring. Appl. Cond. Monit. 19, 1–9 (2022). https://doi.org/10.1007/978-3-030-79519-1_1

  2. Allen, C.W.: A proposed framework for minimizing starts and extending maintenance intervals through optimized scheduling with mixed integer programming. In: Proceedings of the ASME Turbo Expo, p. 9 (2023). https://doi.org/10.1115/gt2023-102032

  3. Arun Prasad, G.K., Panse, C.: Predictive maintenance in forging industry. In: Proceedings of 2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022, pp. 794–800 (2022). https://doi.org/10.1109/iciptm54933.2022.9754058

  4. Azari, M.S., Flammini, F., Santini, S., Caporuscio, M.: A systematic literature review on transfer learning for predictive maintenance in industry 4.0. IEEE Access 11, 12887–12910 (2023). https://doi.org/10.1109/access.2023.3239784

  5. Beduschi, F., et al.: Optimizing rotating equipment maintenance through machine learning algorithm. In: Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference, ADIP 2021 (2021). https://doi.org/10.2118/207657-ms

  6. Bekar, E.T., Nyqvist, P., Skoogh, A.: An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Adv. Mech. Eng. 12(5) (2020). https://doi.org/10.1177/1687814020919207

  7. Biedermann, H., Kinz, A., Bernerstätter, R., Zellner, T.: Lean smart maintenance – implementation in the process industry. Product. Manag. 21(2), 41–43 (2016). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962429211&partnerid=40&md5=8aeb78dcf728f821d50ce89248d3218a

  8. Cahuantzi, R., Chen, X., Güttel, S.: A comparison of LSTM and GRU networks for learning symbolic sequences. In: Arai, K. (ed.) SAI 2023. LNNS, vol. 739, pp. 771–785. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-37963-5_53

  9. Christou, I.T., Kefalakis, N., Zalonis, A., Soldatos, J., Bröchler, R.: End-to-end industrial IoT platform for actionable predictive maintenance. IFAC-PapersOnLine 53(3), 173–178 (2020). https://doi.org/10.1016/j.ifacol.2020.11.028

  10. Cinar, Z.M., Nuhu, A.A., Zeeshan, Q., Korhan, O., Asmael, M., Safaei, B.: Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12(19) (2020). https://doi.org/10.3390/su12198211

  11. Ganga, D., Ramachandran, V.: Adaptive prediction model for effective electrical machine maintenance. J. Qual. Maint. Eng. 26(1), 166–180 (2020). https://doi.org/10.1108/jqme-12-2017-0087

  12. Da Costa, C., Mathias, M.H., Kashiwagi, M.: Development of an instrumentation system embedded on FPGA for real time measurement of mechanical vibrations in rotating machinery. In: Proceedings - 2012 International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2012, vol. 1, pp. 60–64 (2012). https://doi.org/10.1109/msna.2012.6324516

  13. Dalzochio, J., et al.: Machine learning and reasoning for predictive maintenance in industry 4.0: current status and challenges. Comput. Ind. 123 (2020). https://doi.org/10.1016/j.compind.2020.103298

  14. Facchinetti, T., Arazzi, M., Nocera, A.: Time series forecasting for predictive maintenance of refrigeration systems. In: Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on CY (2022). https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927978

  15. Farhat, M.H., Chaari, F., Chiementin, X., Bolaers, F., Haddar, M.: Dynamic remaining useful life estimation for a shaft bearings system. Appl. Cond. Monit. 19 (2022). https://doi.org/10.1007/978-3-030-79519-1_11

  16. Farooq, B., Bao, J.: Machine learning method for spinning cyber-physical production system subject to condition monitoring. In: Luo, Y. (ed.) CDVE 2019. LNCS, vol. 11792, pp. 244–253. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30949-7_28

  17. Franciosi, C., Iung, B., Miranda, S., Riemma, S.: Maintenance for sustainability in the industry 4.0 context: a scoping literature review. IFAC-PapersOnLine 51(11), 903–908 (2018). https://doi.org/10.1016/j.ifacol.2018.08.459

  18. Gupta, K., Tayal, D.K., Jain, A.: An experimental analysis of state-of-the-art time series prediction models. In: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 44–47 (2022). https://doi.org/10.1109/icacite53722.2022.9823455

  19. Hoi, S.C.H., Sahoo, D., Lu, J., Zhao, P.: Online learning: a comprehensive survey. Neurocomputing 459, 249–289 (2021). https://doi.org/10.1016/j.neucom.2021.04.112

  20. Hu, J., Jiang, Z., Wang, H.: Preventive maintenance for a single-machine system under variable operational conditions. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 230(4), 391–404 (2016). https://doi.org/10.1177/1748006x16642332

  21. Hu, J., Zhang, L., Liang, W.: Dynamic degradation observer for bearing fault by MTS-SOM system. Mech. Syst. Signal Process. 36(2), 385–400 (2013). https://doi.org/10.1016/j.ymssp.2012.10.006

  22. Iftikhar, N., Dohot, A.M.: Condition based maintenance on data streams in industry 4.0. In: IN4PL - Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics, pp. 137–144 (2022). https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143048270&partnerid=40&md5=749fe0501a352a29e8647920dcfd3a65

  23. Jamwal, A., Agrawal, R., Sharma, M., Giallanza, A.: Industry 4.0 technologies for manufacturing sustainability: a systematic review and future research directions. Appl. Sci. 11(12) (2021). https://doi.org/10.3390/app11125725

  24. Kanagachidambaresan, G.R., Ruwali, A., Banerjee, D., Prakash, K.B.: Recurrent neural network. In: Prakash, K.B., Kanagachidambaresan, G.R. (eds.) Programming with TensorFlow. EAI/SICC, pp. 53–61. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57077-4_7

  25. Kang, Y., Ju, F.: Integrated analysis of productivity and machine condition degradation: performance evaluation and bottleneck identification. IISE Trans. 51(5), 501–516 (2019). https://doi.org/10.1080/24725854.2018.1494867

  26. Khan, M.A.A., Jamil, M.A., Khanam, S.: Intelligent prediction of multiple defects in rolling element bearing using ANN algorithm. In: 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022 (2022). https://doi.org/10.1109/impact55510.2022.10029154

  27. Khorsheed, R.M., Beyca, O.F.: An integrated machine learning: utility theory framework for real-time predictive maintenance in pumping systems. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 235(5), 887–901 (2021). https://doi.org/10.1177/0954405420970517

  28. Kim, S.-G., Park, D., Jung, J.-Y.: Evaluation of one-class classifiers for fault detection: Mahalanobis classifiers and the Mahalanobis–Taguchi system. Processes 9(8) (2021). https://doi.org/10.3390/pr9081450

  29. Kolar, D., Lisjak, D., Curman, M., Pająk, M.: Condition monitoring of rotary machinery using industrial IoT framework: step to smart maintenance. Tehnicki Glasnik 16(3), 343–352 (2022). https://doi.org/10.31803/tg-20220517173151

  30. Laaradj, S.H., Abdelkader, L., Mohamed, B., Mourad, N.: Vibration-based fault diagnosis of dynamic rotating systems for real-time maintenance monitoring. Int. J. Adv. Manuf. Technol. 126(7–8), 3283–3296 (2023). https://doi.org/10.1007/s00170-023-11320-5

  31. Le-Nguyen, M.-H., Turgis, F., Fayemi, P.-E., Bifet, A.: Exploring the potentials of online machine learning for predictive maintenance: a case study in the railway industry. Appl. Intell. (2023). https://doi.org/10.1007/s10489-023-05092-4

  32. Liao, L., Jin, W., Pavel, R.: Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Trans. Ind. Electron. 63(11), 7076–7083 (2016). https://doi.org/10.1109/tie.2016.2586442

  33. Maasoum, S.M.H., Mostafavi, A., Sadighi, A.: An autoencoder-based algorithm for fault detection of rotating machines, suitable for online learning and standalone applications. In: 6th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2020 (2020). https://doi.org/10.1109/icspis51611.2020.9349574

  34. Maataoui, S., Bencheikh, G., Bencheikh, G.: Predictive maintenance in the industrial sector: a CRISP-DM approach for developing accurate machine failure prediction models. In: 2023 5th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2023, pp. 223–227 (2023). https://doi.org/10.1109/actea58025.2023.10193983

  35. Mahdi, B.E., Ali, E.K., Youssra, E.K., Soufiane, E.: Real time assessment of novel predictive maintenance system based on artificial intelligence for rotating machines. J. Europeen des Systemes Automatises 55(6), 817–823 (2022). https://doi.org/10.18280/jesa.550614

  36. Meddaoui, A., Hain, M., Hachmoud, A.: The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures. Int. J. Adv. Manuf. Technol. 128(7–8), 3685–3690 (2023). https://doi.org/10.1007/s00170-023-12086-6

  37. Mian, T., Choudhary, A., Fatima, S., Panigrahi, B.K.: Artificial intelligence of things based approach for anomaly detection in rotating machines. Comput. Electr. Eng. 109 (2023). https://doi.org/10.1016/j.compeleceng.2023.108760

  38. Nadj, M., Jegadeesan, H., Maedche, A., Hoffmann, D., Erdmann, P.: A situation awareness driven design for predictive maintenance systems: the case of oil and gas pipeline operations. In: 24th European Conference on Information Systems, ECIS 2016 (2016). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995793675&partnerid=40&md5=e3465c646da686345d2bd831a0be0cdb

  39. Naufal, A.N.C.A., et al.: Machine learning as accelerating tool in remote operation realisation through monitoring oil and gas equipments and identifying its failure mode. In: International Petroleum Technology Conference, IPTC 2021 (2021). https://doi.org/10.2523/iptc-21493-ms

  40. Nentwich, C., et al.: Predictive maintenance within the industrial value chain. wt Werkstattstechnik 110(3), 98–102 (2020). https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088297194&partnerid=40&md5=01eec9cb65e3ed451e331eabaf0f53d3

  41. Ogunfowora, O., Najjaran, H.: Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization. J. Manuf. Syst. 70, 244–263 (2023). https://doi.org/10.1016/j.jmsy.2023.07.014

  42. Pandya, D., et al.: Increasing production efficiency via compressor failure predictive analytics using machine learning. In: Proceedings of the Annual Offshore Technology Conference, vol. 1, pp. 47–55 (2018). https://doi.org/10.4043/28990-ms

  43. Patra, K.C., Sethi, R., Behera, D.K.: Anomaly detection in rotating machinery using autoencoders based on bidirectional LSTM and GRU neural networks. Turk. J. Electr. Eng. Comput. Sci. 30(4), 1637–1653 (2022). https://doi.org/10.55730/1300-0632.3870

  44. Patwardhan, A., Verma, A.K., Kumar, U.: A survey on predictive maintenance through big data. In: Kumar, U., Ahmadi, A., Verma, A., Varde, P. (eds.) Current Trends in Reliability, Availability, Maintainability and Safety. LNME, pp. 437–445. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23597-4_31

  45. Phan, T.L., Gehrhardt, I., Heik, D., Bahrpeyma, F., Reichelt, D.: A systematic mapping study on machine learning techniques applied for condition monitoring and predictive maintenance in the manufacturing sector. Logistics 6(2) (2022). https://doi.org/10.3390/logistics6020035

  46. Purnachand, k., Shabbeer, M., Syamala Rao, P.N.V.M., Babu, C.M.: Predictive maintenance of machines and industrial equipment. In: Proceedings - 2021 IEEE 10th International Conference on Communication Systems and Network Technologies, CSNT 2021, pp. 318–324 (2021). https://doi.org/10.1109/csnt51715.2021.9509696

  47. Rodrigues, J., Farinha, J.T., Cardoso, A.M.: Predictive maintenance tools – a global survey. WSEAS Trans. Syst. Control 16, 96–109 (2021). https://doi.org/10.37394/23203.2021.16.7

  48. Rossen, A.: On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations. Jahrbucher fur Nationalokonomie und Statistik 236(3), 389–409 (2016). https://doi.org/10.1515/jbnst-2015-1019

  49. Samsuri, N.A., Raman, S.A., Tuan Ya, T.M.Y.S.: Evaluation of NARX network performance on the maintenance application of rotating machines. In: Ahmad, F., Al-Kayiem, H.H., King Soon, W.P. (eds.) ICPER 2020. LNME pp. 593–609. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-1939-8_46

  50. Satishkumar, R., Sugumaran, V.: Estimation of remaining useful life of bearings based on support vector regression. Indian J. Sci. Technol. 9(10) (2016). https://doi.org/10.17485/ijst/2016/v9i10/88997

  51. Senanayaka, A., et al.: Similarity-based multi-source transfer learning approach for time series classification. Int. J. Progn. Health Manag. 13(2) (2022). https://doi.org/10.36001/ijphm.2022.v13i2.3267

  52. Shah, J., Wang, W.: An evolving neuro-fuzzy classifier for fault diagnosis of gear systems. ISA Trans. 123, 372–380 (2022). https://doi.org/10.1016/j.isatra.2021.05.019

  53. Shi, H., Zhang, J., Zio, E., Zhao, X.: Opportunistic maintenance policies for multi-machine production systems with quality and availability improvement. Reliab. Eng. Syst. Saf. 234 (2023). https://doi.org/10.1016/j.ress.2023.109183

  54. Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., Cesarotti, V.: Maintenance transformation through industry 4.0 technologies: a systematic literature review. Comput. Ind. 123 (2020). https://doi.org/10.1016/j.compind.2020.103335

  55. Soundarrajan, C., Duraisamy, R.N., Jayabalan, M., Govindharajan, G., Gopalakrishnan, P., Shanmugam, S.K.: Short term predictive maintenance using machine learning models. In: AIP Conference Proceedings, vol. 2764, no. 1 (2023). https://doi.org/10.1063/5.0173800

  56. Ton, B., et al.: PrimaVera: synergising predictive maintenance. Appl. Sci. 10(23), 1–19 (2020). https://doi.org/10.3390/app10238348

  57. Vaerenbergh, S.V., Santamaría, I.: Online regression with kernels. In: Regularization, Optimization, Kernels, and support Vector Machines, pp. 477–501 (2014). https://doi.org/10.1201/b17558-24

  58. Von Enzberg, S., Naskos, A., Metaxa, I., Köchling, D., Kühn, A.: Implementation and transfer of predictive analytics for smart maintenance: a case study. Front. Comput. Sci. 2 (2020). https://doi.org/10.3389/fcomp.2020.578469

  59. Zonta, T., et al.: Predictive maintenance in the industry 4.0: a systematic literature review. Comput. Ind. Eng. 150 (2020). https://doi.org/10.1016/j.cie.2020.106889

Download references

Author information

Authors and Affiliations

  1. ENSAM-Meknes, Moulay Ismail University, Meknes, Morocco

    Hassana Mahfoud, Oussama Moutaoukil & Mohammed Toum Benchekroun

  2. National School of Applied Sciences, University of Cadi Ayyad, Marrakesh, Morocco

    Adnane Latif

Authors
  1. Hassana Mahfoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oussama Moutaoukil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Toum Benchekroun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adnane Latif
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hassana Mahfoud .

Editor information

Editors and Affiliations

  1. Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mahfoud, H., Moutaoukil, O., Toum Benchekroun, M., Latif, A. (2024). Real-Time Predictive Maintenance-Based Process Parameters: Towards an Industrial Sustainability Improvement. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 931. Springer, Cham. https://doi.org/10.1007/978-3-031-54288-6_3

Download citation

Publish with us

Policies and ethics

Search

Navigation