Advertisement

Predictive Models for Maintenance Optimization: An Analytical Literature Survey of Industrial Maintenance Strategies

  • Oana MerktEmail author
Conference paper
  • 17 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 380)

Abstract

As machine learning (ML) techniques and sensor technology continue to gain importance, the data-driven perspective has become a relevant approach for improving the quality of maintenance for machines and processes in industrial environments. Our study provides an analytical literature review of existing industrial maintenance strategies showing first that, among all extant approaches to maintenance, each varying in terms of efficiency and complexity, predictive maintenance best fits the needs of a highly competitive industry setup. Predictive maintenance is an approach that allows maintenance actions to be based on changes in the monitored parameters of the assets by using a variety of techniques to study both live and historical information to learn prognostic data and make accurate predictions. Moreover, we argue that, in any industrial setup, the quality of maintenance improves when the applied data-driven techniques and methods (i) have economic justifications and (ii) take into consideration the conformity with the industry standards. Next, we consider ML to be a prediction methodology, and we show that multimodal ML methods enhance industrial maintenance with a critical component of intelligence: prediction. Based on the surveyed literature, we introduce taxonomies that cover relevant predictive models and their corresponding data-driven maintenance techniques. Moreover, we investigate the potential of multimodality for maintenance optimization, particularly the model-agnostic data fusion methods. We show the progress made in the literature toward the formalization of multimodal data fusion for industrial maintenance.

Keywords

Maintenance strategies Predictive maintenance Multimodal machine learning Predictive models Data fusion CRISP_DM Industrial Data Space 

References

  1. 1.
    Susto, G.A., Mcloone, S., Pampuri, S., Benghi, A., Schirru, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Informat. 11(3), 812–820 (2015).  https://doi.org/10.1109/TII.2014.2349359CrossRefGoogle Scholar
  2. 2.
    Liu, Z., Norbert, M., Nezih, M.: The role of Data Fusion in predictive maintenance using Digital Twin. AIP Conf. Proc. 1949(1), 020023 (2018).  https://doi.org/10.1063/1.5031520CrossRefGoogle Scholar
  3. 3.
    Manco, G., et al.: Fault detection and explanation through big data analysis on sensor streams. Expert Syst. Appl. 87, 141–156 (2018).  https://doi.org/10.1016/j.eswa.2017.05.079CrossRefGoogle Scholar
  4. 4.
    Niu, G., Li, H.: IETM centered intelligent maintenance system integrating fuzzy semantic inference and data fusion. Microelectron. Reliab. 75, 197–204 (2017).  https://doi.org/10.1016/j.microrel.2017.03.015CrossRefGoogle Scholar
  5. 5.
    Guo, L., Li, N., Jia, F., Lei, Y., Lin, J.: A recurrent neural network-based health indicator for remaining useful life prediction of bearings. Neurocomputing 240, 98–109 (2017).  https://doi.org/10.1016/j.neucom.2017.02.045CrossRefGoogle Scholar
  6. 6.
    Acorsi, R., Manzini, R., Pascarella, P., Patella, M., Sassi, S.: Data Mining and Machine Learning for Condition-based Maintenance. In: (eds.) Proceedings of the 2017 International Conference on Flexible Automation and Intelligent Manufacturing FAIM, 27–30 June 2017, Modena, Italy, pp. 1153-1161 (2017).  https://doi.org/10.1016/j.promfg.2017.07.239CrossRefGoogle Scholar
  7. 7.
    Safizadeh, M., Latifi, S.: Using multisensory data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf. Fusion 18(1), 1–8 (2014).  https://doi.org/10.1016/j.inffus.2013.10.002CrossRefGoogle Scholar
  8. 8.
    Schmidt, B., Sandberg, U., Wang, U.: Next generation condition based Predictive Maintenance. Methods 13306, 4–11 (2014)Google Scholar
  9. 9.
    Schenk, M.: Instandhaltung technischer Systeme. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-03949-2CrossRefGoogle Scholar
  10. 10.
    Otto, B., et al.: Industrial Data Space – Digital soveregnity over data, In: Fraunhofer Gesellschaft zur Förderung der angewandten Forschung (2016)Google Scholar
  11. 11.
    Diez-Olivan, A., del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 40. Information Fusion 50, 92–111 (2019).  https://doi.org/10.1016/j.inffus.2018.10.005CrossRefGoogle Scholar
  12. 12.
    DIN EN-13306. DIN Standards – Maintenance terminology, Beuth Publishing DIN (2018). https://dx.doi.org/10.31030/2641990
  13. 13.
    DIN EN-31051. DIN Standards – Fundamentals of maintenance, Beuth Publishing DIN (2019). https://dx.doi.org/10.31030/3048531
  14. 14.
    Baltrusaitis, T., Ahuja, C., Morency, L.: Multimodal Machine Learning: A Survey and Taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423–443 (2019).  https://doi.org/10.1109/TPAMI.2018.2798607CrossRefGoogle Scholar
  15. 15.
    Alpaydin, E.: Classifying multimodal data. In: Oviatt, S., Schuller, B., Cohen, P.R., Sonntag, D., Potamianos, G., Krüger, A. (eds.) The Handbook of Multimodal-Multisensor Interfaces, In Association for Computing Machinery and Morgan & Claypool, NY, pp. 49–69 (2018)Google Scholar
  16. 16.
    Noman, N.A., Nasr, E.S.A., AlShayea, A., Kaid, H.: Overview of predictive condition based maintenance research using bibliometric indicators. J. K. Saud Univ. Eng. Sci. 31(4), 355–367 (2019)Google Scholar
  17. 17.
    Oates, B.J.: Researching Information Systems and Computing. Sage Publications Ltd., Thousand Oaks (2006)Google Scholar
  18. 18.
    Peffers, K., Tuunanen, T., Rothenberger, M., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–77 (2007).  https://doi.org/10.2753/MIS0742-1222240302CrossRefGoogle Scholar
  19. 19.
    Bunks, C., McCarthy, D., Al-Ani, T.: Condition-based Maintenance of machines using hidden Markov Models. Mech. Syst. Sign. Process. 14(4), 597–612 (2000).  https://doi.org/10.1006/mssp.2000.1309CrossRefGoogle Scholar
  20. 20.
    Deuszkiewick, P., Radkowski, S.: On-line condition monitoring of a power transmission unit of a rail vehicle. Mech. Syst. Sign. Process. 17(6), 1321–1334 (2003).  https://doi.org/10.1006/mssp.2002.1578CrossRefGoogle Scholar
  21. 21.
    Hao, Y., Sun, J., Yang, G., Bai, J.: The application of support vector machines to gas turbines performance diagnosis. Chinese J. Aeronaut. 18(1), 15–19 (2005).  https://doi.org/10.1016/S1000-9361(11)60276-8CrossRefGoogle Scholar
  22. 22.
    Baraldi, P., Zio, E., di Maio, F.: Unsupervised clustering for fault diagnostics in nuclear power plants components. Int. J. Comp. Intell. Syst. 6(4), 764–777 (2014).  https://doi.org/10.1080/18756891.2013.804145CrossRefGoogle Scholar
  23. 23.
    Merkt, O.: On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: an Analytical Literature Review of Maintenance Strategies. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2019 Federated Conference on Computer Science and Information Systems FedCSIS, 1–4 September, pp. 693-704. Leipzig University, Leipzig, Germany (2019). https://dx.doi.org/10.15439/2019F101
  24. 24.
    Alexandru, A.: Using Expert Systems for Fault Detection and Diagnosis. Industrial Applications (1998)Google Scholar
  25. 25.
    Krishnakumari, A., Elayaperumal, A., Saravanan, M., Arvindan, C.: Fault diagnostics of spur gear using decision tree and fuzzy classifier. Int. J. Adv. Manuf. Technol. 89(9–12), 3487–3494 (2017).  https://doi.org/10.1007/s00170-016-9307-8CrossRefGoogle Scholar
  26. 26.
    Jaramillo, V.H., Ottewill, J.R., Dudek, R., Lepiarczyk, D., Pawlik, P.: Condition monitoring of distributed systems using two-stage Bayesian inference data fusion. Mech. Syst. Sign. Process. 87, 91–110 (2017).  https://doi.org/10.1016/j.ymssp.2016.10.004CrossRefGoogle Scholar
  27. 27.
    Liu, C., Li, Y., Zhou, G., Shen, W.: A sensor fusion and support vector machine-based approach for recognition of complex machining conditions. J. Intell. Manuf. 29(8), 1739–1752 (2018).  https://doi.org/10.1007/s10845-016-1209-yCrossRefGoogle Scholar
  28. 28.
    Diez, A., Khoa, N.L.D., Alamdari, M.M., Wang, Y., Chen, F., Runcie, P.: A clustering approach for structural health monitoring on bridges. J. Civil Struct. Health Monit. 6(3), 429–445 (2016)CrossRefGoogle Scholar
  29. 29.
    Li, C., Sánchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D.: Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 16(6), 895, 1–19 (2016)CrossRefGoogle Scholar
  30. 30.
    Widmer, T., Klein, A., Wachter, P., Meyl, S.: Predicting Material Requirements in the Automotive Industry Using Data Mining. In: Abramowicz, W., Corchuelo, R. (eds.) BIS 2019. LNBIP, vol. 354, pp. 147–161. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-20482-2_13CrossRefGoogle Scholar
  31. 31.
    Mosallam, A., Medjaher, K., Zerhouni, N.: Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J. Intell. Manuf. 27(5), 1037–1048 (2016).  https://doi.org/10.1007/s10845-014-0933-4CrossRefGoogle Scholar
  32. 32.
    Alsina, E.F., Chica, M., Trawinski, K., Regattieri, A.: On the use of Machine Learning methods to predict component reliability from data-driven industrial case studies. Int. J. Adv. Manuf. Technol. 94(5–8), 2419–2433 (2018).  https://doi.org/10.1007/s00170-017-1039-xCrossRefGoogle Scholar
  33. 33.
    Cristaldi, L., Leone, G., Ottoboni, R., Subbiah, S., Turrin, S.: A comparative study on data-driven prognostic approaches using fleet knowledge. In: Arpaia, A., Catelani, M., Cristaldi, L. (eds.) Proceedings of the 2016 IEEE International Conference on Instrumentation and Measurement Technology (I2MTC), 23–26 May, 2016, Taipei, Taiwan, pp. 1-6 (2016).  https://doi.org/10.1109/I2MTC.2016.7520371
  34. 34.
    Liu, Q. (C.), Wang, H.P. (B.): A case study on multisensory data fusion for imbalanced diagnosis of rotating machinery. AI EDAM 15(3), 203–2010 (2001)Google Scholar
  35. 35.
    Xenakis, A., Karageorgos, A., Lallas, E., Chis, A.E., Gonzalez-Velez, H.: Towards distributed IoT/cloud based fault detection and maintenance in industrial automation. In: Shakshuki, M.E., Yasar, A.-U.-H. (eds.) Proceedings of the 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019), April 29 - May 2, 2019, Leuven, Belgium, pp. 683–690 (2019).  https://doi.org/10.1016/j.procs.2019.04.091CrossRefGoogle Scholar
  36. 36.
    Sobaszek, Ł., Gola, A., Kozłowski, E.: Application of survival function in robust scheduling of production jobs. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2017 Federated Conference on Computer Science and Information systems FedCSIS, 3–6 September 2017, pp. 575-578. Czech Technical University in Prague, Prague (2017). http://dx.doi.org/10.15439/2017F276
  37. 37.
    Sobaszek, Ł., Gola, A., Kozłowski, E.: Job-shop scheduling with machine breakdown prediction under completion time constraint. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2018 Federated Conference on Computer Science and Information Systems FedCSIS, 9–12 September 2018, pp. 437-440. Adam Mickiewicz university Poznan, Poland (2018). http://dx.doi.org/10.15439/2018F83
  38. 38.
    Khaleghi, B., Karray, F., Khamis, A., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14, 28–44 (2013).  https://doi.org/10.1016/j.inffus.2011.08.001CrossRefGoogle Scholar
  39. 39.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives, Technical report. Univ. Montreal, 35(8), 1798–1828 (2013).  https://doi.org/10.1109/TPAMI.2013.50CrossRefGoogle Scholar
  40. 40.
    Baban, C.F., Baban, M., Suteu, M.D.: Using a fuzzy logic approach for the predictive maintenance of textile machines. J. Intell. Fuzzy Syst. 30(2), 999–1006 (2016).  https://doi.org/10.3233/IFS-151822CrossRefGoogle Scholar
  41. 41.
    Cui, W., Lu, Z., Li, C., Han, X.: A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops. Comput. Ind. Eng. 115, 342–353 (2018).  https://doi.org/10.1016/j.cie.2017.11.020CrossRefGoogle Scholar
  42. 42.
    Seidgar, H., Zandieh, M., Mahdavi, I.: An efficient metaheuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach. Int. J. Ind. Syst. Eng. 26(1), 16–41 (2017).  https://doi.org/10.1504/IJISE.2017.083180CrossRefGoogle Scholar
  43. 43.
    Chou, C.-A., Jin, X., Müller, A., Ostadabbas, S.: MMDF 2018 Multimodal Data Fusion Workshop Report. Northeastern University, Boston (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Hohenheim UniversityStuttgartGermany

Personalised recommendations