Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: An Analytical Literature Review of Maintenance Strategies

DOI: http://dx.doi.org/10.15439/2019F101

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 693704 ()

Full text

Abstract. Due to advances in machine learning techniques and sensor technology, the data driven perspective is nowadays the preferred approach for improving the quality of maintenance for machines and processes in industrial environments. Our study reviews existing maintenance works by highlighting the main challenges and benefits and consequently, it shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. Moreover, we argue that in any industrial setup the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. In order to classify the existing maintenance strategies, we explore the entire data driven model development life cycle: data acquisition and analysis, data modeling, data fusion and model evaluation. Based on the surveyed literature we introduce taxonomies that cover relevant predictive models and their corresponding data driven maintenance techniques.

References

  1. G. A. Susto, S. Mcloone, S. Pampuri, A. Benghi, and A. Schirru, "Machine Learning for Predictive Maintenance: A Multiple Classifier Approach", IEEE Transactions on Ind. Inf. 11(3), 2015, pp. 812-820, https://doi.org/10.1109/TII.2014.2349359.
  2. Z. Liu, M. Norbert, and M. Nezih, The role of Data Fusion in predictive maintenance using Digital Twin, in AIP Conference Proceedings 1949(1):02023, 2018, https://doi.org/10.1063/1.5031520.
  3. G. Manco, E. Ritacco, P. Rullo, L. Galluci, W. Astill, D. Kimber, and M. Antoneli,Fault detection and explanation through big data analysis on sensor streams, in Expert Syst. Appl. 87, 2017, pp. 141-156, https://doi.org/10.1016/j.eswa.2017.05.079.
  4. G. Niu and H. Li, IETM centered intelligent maintenance system integrating fuzzy semantic inference and data fusion, in Microelectron. Reliab. 75, 2017, pp. 197-204, https://doi.org/10.1016/j.microrel.2017.03.015.
  5. T. Widmer, A. Klein, P. Wachter, and S. Meyl, Predicting Material Requirements in the Automotive Industry using Data Mining, in BIS, 2019, pp. 582-588,
  6. Ł. Sobaszek, A. Gola, and E. Kozłowski, Application of survival function in robust scheduling of production jobs, in FedCSIS 2017, ACSIS, Vol. 11, 2017, pp. 575-578, http://dx.doi.org/10.15439/2017F276.
  7. Ł. Sobaszek, A. Gola, and E. Kozłowski, Job-shop scheduling with machine breakdown prediction under completion time constraint, in FedCSIS 2018, ACSIS, Vol. 15, 2018, pp. 437-440, http://dx.doi.org/10.15439/2018F83.
  8. L. Guo, N. Li, F. Jia, Y. Lei, and J. Lin, A recurrent neural network based health indicator for remaining useful life prediction of bearings, in Neurocomputing 240, 2017, pp. 98-109, https://doi.org/10.1016/j.neucom.2017.02.045.
  9. R. Acorsi, R. Manzini, P. Pascarella, M. Patella, and S. Sassi, Data Mining and Machine Learning for Condition-based Maintenance, in Int. Conf. on Flexible Automation and Intelligent Manufacturing 11, 2017, pp. 1153-1161, https://doi.org/10.1016/j.promfg.2017.07.239.
  10. M. Safizadeh and S. Latifi, Using multisensory data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell, in Inf. Fusion 18, 2014, pp. 1-8, https://doi.org/10.1016/j.inffus.2013.10.002.
  11. B. Schmidt, U. Sandberg and, L. Wang, Next generation condition based Predictive Maintenance, in Methods 13306, 2014, pp. 4-11.
  12. M. Schenk, Instandhaltung technicher Systeme, 2010.
  13. B. Otto, S. Auer, J. Cirullies, J. Jürjens, N. Menz, J. Schon, and S. Wenzel, Industrial Data Space Digital soveregnity over data, in Fraunhofer Gesellschaft zur Förderung der angewandten Forschung, 2016.
  14. DIN EN-13306. DIN Standards Publication Maintenance Begriffe der Instandhaltung/Maintenance terminology, 2010.
  15. DIN EN-31051. DIN Standards Publication Maintenance Grundlage der Instandhaltung/Fundamentals of Maintenance, 2012.
  16. A.R. Hevner, S.T. March, J. Park, and S. Ram, Design science in information system research, MIS Q. 28(1), 2004, pp. 75-105.
  17. B. J. Oates, Researching Information Systems and Computing, Sage Publications Ltd., 2006.
  18. K. Peffers, T. Tuunanen, M. Rothenberger and S. Chatterjee, A Design Science Research Methodology for Information Systems Research, in J. Manage. Inf. Syst. 24(3), 2007, pp. 45-77, https://doi.org/10.2753/MIS0742-1222240302.
  19. C. Bunks, D. McCarthy, and T. Al-Ani, Condition-based Maintenance of machines using hidden Markov Models, in NAMRC 32, 2004, pp. 597-612, https://doi.org/10.1006/mssp.2000.1309.
  20. P. Deuszkiewick and S. Radkowski, On-line condition monitoring of a power transmission unit of a rail vehicle, in Mechanical Systems and Signal Processing 17(6), 2003, pp. 1321-1334, https://doi.org/10.1006/mssp.2002.1578.
  21. Y. Hao, J. Sun, G. Yang and J. Bai, The Application of Support Vector Machines to Gas Turbines Performance Diagnosis, in Chinese Journal of Aeronautics 18 (1), 2005, pp. 15-19, https://doi.org/10.1016/S1000-9361(11)60276-8.
  22. P. Baraldi, E. Zio and F. di Maio, Unsupervised Clustering for Fault Diagnostics in Nuclear Power Plants Components, in Int. Journal of Comp. Intelligent Systems 6(4), 2014, pp. 764-777, https://doi.org/10.1080/18756891.2013.804145.
  23. A. Alexandru, Using Expert Systems for Fault Detection and Diagnosis in Industrial Applications, 1998.
  24. A. Krishnakumari, A. Elayaperumal, M. Saravanan, and C. Arvindan Fault diagnostics of spur gear using decision tree and fuzzy classifier, in Int. J. Adv. Manuf. Technol. 89 (9-12), 2017, pp. 3487-3494, https://doi.org/10.1007/s00170-016-9307-8.
  25. V.H. Jaramillo, J.R. Ottewill, R. Dudek, D. Lepiarczyk, and P. Pawlik, Condition monitoring of distributed systems using two-stage Bayesian inference data fusion, in Mech. Syst. Signal Process. 87, 2017, pp. 91-110, https://doi.org/10.1016/j.ymssp.2016.10.004.
  26. C. Liu, Y. Li, G. Zhou, and W. Shen, A sensor fusion and support vector machine based approach for recognition of complex machining conditions, in Journal of Intelligent Manufacturing, 2016, pp. 1-14, https://doi.org/10.1007/s10845-016-1209-y.
  27. A. Diez, N.L.D. Khoa, M.M. Alamdari, Y. Wang, F. Chen, and P. Runcie, A clustering approach for structural health monitoring on bridges, in J. Civil Struct. Health Monitoring 6 (3), 2016, pp. 429-445.
  28. C. Li, R.-V. Sánchez, G. Zurita, M. Cerrada, and D. Cabrera, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning, in Sensors 16 (6): 895, 2016, pp. 1-19.
  29. Q. (C.) Liu and H.P. (B.) Wang, A case study on multisensory data fusion for imbalanced diagnosis of rotating machinery, in AI EDAM 15(3), 2001, pp. 203-2010.
  30. A. Xenakis, A. Karageorgos, E. Lallas, A.E. Chis, and H. Gonzalez-Velez, Towards Distributed IoT/Cloud based Fault Detection and Maintenance in Industrial Automation, in EDI40, 2019, pp. 683-690, https://doi.org/10.1016/j.procs.2019.04.091.
  31. A. Mosallam, K. Medjaher, and N. Zerhouni, Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction, in J. Intell. Manuf. 27 (5), 2016, pp. 1037-1048, https://doi.org/10.1007/s10845-014-0933-4.
  32. E. F. Alsina, M. Chica, K. Trawinski, and A. Regattieri, On the use of Machine Learning methods to predict component reliability from data-driven industrial case studies, in Int. J. Adv. Manufacturing Technology, (94), 2018, pp. 2419-2433, https://doi.org/10.1007/s00170-017-1039-x.
  33. L. Cristaldi, G. Leone, R. Ottoboni, S. Subbiah, and S. Turrin, A comparative study on data-driven prognostic approaches using fleet knowledge, in IEEE International Conference on Instrumentation and Measurement Technology (I2MTC), 2016, pp. 1-6, https://doi.org/10.1109/I2MTC.2016.7520371.
  34. C. F. Baban, M. Baban, and M.D. Suteu, Using a fuzzy logic approach for the predictive maintenance of textile machines, in J. Intell. Fuzzy Syst. 30 (2), 2016, pp. 999-1006, https://doi.org/10.3233/IFS-151822.
  35. W. Cui, Z. Lu, C. Li, and X. Han, A proactive approach to solve integrated production scheduling and maintenance planning problem in flow shops, in Comput. Ind. Eng. 115, 2018, pp. 342-353, https://doi.org/10.1016/j.cie.2017.11.020.
  36. T. Baltrusaitis, C. Ahuja, and L. Morency, Multimodal Machine Learning: A Survey and Taxonomy, in IEEE transactions on pattern analysis and machine intelligence, 2017, pp. 423-443, https://doi.org/10.1109/TPAMI.2018.2798607.
  37. E. Alpaydin, Classifying multimodal data" in The Handbook of Multimodal-Multisensor Interfaces, Ed. Sharon Oviatt, Björn Schuller, Philip R. Cohen, Daniel Sonntag, Geranimos Potamianos, and Antonio Krüger, in Association for Computing Machinery and Morgan & Claypool, NY, 2018, pp. 49-69, https://10.1145/3107990.3107994.
  38. B. Khaleghi, F. Karray, A. Khamis, and S. N. Razavi, Multisensor Data Fusion: A review of the State-of-the-Art, in Information Fusion 14, 2013, pp. 28-44, https://doi.org/10.1016/j.inffus.2011.08.001.
  39. Y. Bengio, A. Courville, and P. Vincent, Representation learning: a review and new perspectives, Technical report. U Montreal, 35(8), pp. 1798-1828, 2013, https://doi.org/10.1109/TPAMI.2013.50.
  40. N. Srivastava and R. Salakhutdinov, Multimodal learning with Multi-modal Boltzmann Machines, in Advances in Neural Information Processing Systems, 2012, pp. 2222-2230.
  41. A. Zheng, Evaluating machine Learning Models A Beginners Guide to Key Concepts and Pitfalls. OReilly Media, 2015, ISBN 978-1-491-93246-9.
  42. A. Ng, Machine Learning, Online Course offered by Stanford University, 2010.
  43. B. Schmidt, W. Lihui, and D. Galar, Semantic Framework for PdM in a cloud environment, in CIRP ICME 62, 2017, pp. 582-588, https://doi.org/10.1016/j.procir.2016.06.047.
  44. H. Seidgar, M. Zandieh, and I. Mahdavi, An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach, in Int. J. Ind. Syst. Eng. 26(1), 2017, pp. 16-41, https://doi.org/10.1504/IJISE.2017.083180.
  45. A. Diez-Olivan, J. del Ser, D. Galar, and B. Sierra, Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0, in Int. J. Inf. Fusion 50, 2019, pp. 92-111, https://doi.org/10.1016/j.inffus.2018.10.005.
  46. P.H. Foo and G.W. Ng, High-level Information Fusion: An Overview, in Journal of Advances in Information Fusion, 8(1), 2013, pp. 33-72, https://doi.org/10.1.1.360.6651.
  47. C.-A. Chou, X. Jin, A. Müller, and S. Ostadabbas, (MMDF) Multimodal Data Fusion Workshop Report, 2018.