1. Proceedings
  2. I3M
  3. 2019
  4. MAS
  5. 10.46354/i3m.2019.mas.007

Machine learning techniques applied to industrial engineering: a multi criteria approach

  • a Fabio De Felice  , 
  • b Antonella Petrillo  , 
  • c Marta Travaglioni  , 
  • d Giuseppina Piscitelli  , 
  • e Raffaele Cioffi 
  • a Department of Civil and Mechanical Engineering - University of Cassino and Southern Lazio, Italy
  • bcde Department of Engineering - University of Naples “Parthenope”, Italy
Cite as
De Felice F., Travaglioni M., Piscitelli G., Cioffi R., Petrillo A. (2019). Machine learning techniques applied to industrial engineering: a multi criteria approach. Proceedings of the 18th International Conference on Modelling and Applied Simulation (MAS 2019), pp. 44-54.
DOI: https://doi.org/10.46354/i3m.2019.mas.007.
 Download PDF

Abstract

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

References

  1. Petrillo, A., De Felice, F., Zomparelli, F., 2019. Performance measurement for world class
    manufacturing: a model for Italian automotive industry. Total Quality Management & Business
    Excellence, Pages 908-935, Volume 30, 2019 -Issue 7-8.
  2. Fernandes M., Canito A., Bolón-Canedo V., Conceição L., Praça I., Marreiros G., 2019. Data analysis and feature selection for predictive maintenance: a case-study in the metallurgic industry. International Journal of Information Management, 46, 252-262.
  3. Fernandes M., Canito A., Bolón-Canedo V., Conceição L., Praça I., Marreiros G., 2019. Data analysis and feature selection for predictive maintenance: a case-study in the metallurgic industry. International Journal of Information Management, 46, 252-262.
  4. Lee C.Y., Huang T.S., Liu M.K., Lan C.Y., 2019. Data science for vibration heteroscedasticity and
    predictive maintenance of rotary bearings. Energies, 12 (5), art. no. 801.
  5. Serio L., Antonello F., Baraldi P., Castellano A., Gentile U., Zio E., 2018. A smart framework for
    the availability and reliability assessment and management of accelerators technical facilities.
    Journal of Physics: Conference Series, 1067 (7), art. no. 072029.
  6. Kleftogiannis D., Ashoor H., Bajic V.B., 2018. TELS: a novel computational framework for identifying motif signatures of transcribed enhancers. Genomics, Proteomics and Bioinformatics, 16 (5), 332-341.
  7. Bedi J., Toshniwal D., 2018. Empirical mode decomposition based deep learning for electricity
    demand forecasting. IEEE Access, 6, 49144-49156.
  8. Shafi U., Safi A., Shahid A.R., Ziauddin S., Saleem M.Q., 2018. Vehicle remote health monitoring and prognostic maintenance system. Journal of Advanced Transportation, 2018, art. no. 8061514.
  9. Pandit R., Infield D., 2018. Gaussian process operational curves for wind turbine condition monitoring. Energies, 11 (7), art. no. 1631.
  10. Ullah I., Yang F., Khan R., Liu L., Yang H., Gao B., Sun K., 2017. Predictive maintenance of power
    substation equipment by infrared thermography using a machine-learning approach. Energies, 10 (12), art. no. 1987.
  11. Wang D., Tsui K.-L., Miao Q., 2017. Prognostics and health management: a review of vibration based bearing and gear health indicators. IEEE Access, 6, 665-676.
  12. Wilson D.L., Coyle J.R., Thomas E.A., 2017. Ensemble machine learning and forecasting can achieve 99% uptime for rural handpumps. PLoS ONE, 12 (11), art. no. e0188808.
  13. Wu D., Jennings C., Terpenny J., Gao R.X., Kumara S., 2017. A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 139 (7), art. no.071018.
  14. Zhou F., Gao Y., Wen C., 2017. A novel multimode fault classification method based on deep learning. Journal Of Control Science And Engineering, 2017, art. no. 3583610.
  15. Wang C., Liu X., Liu H., Chen Z., 2016. A fault diagnostic method for position sensor of switched
    reluctance wind generator, Journal Of Electrical Engineering And Technology, 11 (1), 29-37.
  16. Cartella F., Lemeire J., Dimiccoli L., Sahli H., 2015. Hidden semi-markov models for predictive
    maintenance. Mathematical Problems in Engineering, 2015, art. no. 278120.
  17. Tulabandhula T., Rudin C., 2014. On combining machine learning with decision making. Machine Learning, 97 (1-2), 33-64.
  18. Susto, G.A., Schirru A., Pampuri S., McLoone S., Beghi A., 2015. Machine learning for predictive
    maintenance: a multiple classifier approach, IEEE Transactions on Industrial Informatics, 11 (3), 812-820.
  19. Leahy K., Gallagher C., O'Donovan P., Bruton K., O'Sullivan D.T.J., 2018. A robust prescriptive
    framework and performance metric for diagnosing and predicting wind turbine faults based on scada and alarms data with case study, Energies 11 (7), art. no. 1738.
  20. Peres R.S., Rocha A.D., Leitao P., Barata J., 2018. IDARTS - Towards intelligent data analysis and
    real-time supervision for industry 4.0, Computers in Industry, 101, 138-146.
  21. Wu Z.Y., Luo H., Yang Y.N., Lv P., Zhu X.N., Ji Y., Wu B.A., 2018. K-PdM: KPI-oriented machinery
    deterioration estimation framework for predictive maintenance using cluster-based hidden markov model. IEEE Access, 6, 41676-41687.
  22. Mohamudally N., Peermamode-Mohaboob M., 2018. Building an anomaly detection engine (ade) for iot smart applications. Procedia Computer Science, 134, 10-17.
  23. Uhlmann E., Pontes R.P., Geisert C., Hohwieler E., 2018. Cluster identification of sensor data for
    predictive maintenance in a Selective Laser Melting machine tool. Procedia Manufacturing,
    24, 60-65.
  24. Wegener K., Gittler T., Weiss L., 2018. Dawn of new machining concepts: compensated, intelligent, bioinspired. Procedia CIRP, 77, 1-17.
  25. Zhang J., Wang P., Yan R., Gao R.X., 2018. Deep Learning for improved system remaining life
    prediction. Procedia CIRP, 72, 1033-1038.
  26. Zuev D., Kalistratov A., Zuev A., 2018. Machine Learning in IT Service Management. Procedia
    Computer Science, 145 (2018) 675–679.
  27. Schmidt B., Wang L., 2018. Predictive Maintenance of machine tool linear axes: a case from
    manufacturing industry. Procedia Manufacturing,17, 118-125.
  28. Pandey S.K., Mishra R.B., Triphathi A.K., 2018. Software bug prediction prototype using bayesian network classifier: a comprehensive model. Procedia Computer Science, 132, 1412-1421.
  29. Chakravorti N., Rahman M.M., Sidoumou M.R., (...), Gosewehr F., Wermann J., 2018. Validation of PERFoRM reference architecture demonstrating an application of data mining for predicting
    machine failure. Procedia CIRP, 72, 1339-1344.
  30. Cortes C., Vapnik V., 1995. Support-vector networks. Machine Learning, 20 (3), 273-297.
  31. Kotsiantis S. B., Zaharakis I., Pintelas P., 2007. Supervised machine learning: a review of
    classification techniques. Emerging artificial intelligence applications in computer engineering,
    160, 3-24.
  32. Marasco D.E., Kontokosta C.E., 2016. Applications of machine learning methods to identifying and predicting building retrofit opportunities. Energy and Buildings, 128, 431-441.
  33. Lamirel J.-C., Al Shehabi S., Hoffmann, M., François C., 2003. Intelligent patent analysis through the use of a neural network. Proceedings of the ACL-2003 Workshop on Patent Corpus Processing, 20, 7–23.
  34. Markovic D., Petkovic D., Nikolic V., Milovancevic M., Petkovic B., 2017. Soft computing prediction of economic growth based in science and technology factors, Phys. Stat. Mech. Appl. 465, 217–220.
  35. Choi J., Jang D., Jun S., Park S., 2015. A predictive model of technology transfer using patent analysis. Sustainability, 7 (12), 16175-16195.
  36. Searle, J. R., 1980. Minds, brains, and programs. Behavioral and Brain Sciences 3 (3), 417-457.
  37. Samuel A. L., 1959. Some studies in machine learning using the game of checkers. BM Journal of Research and Development archive, 3 (3), 210-229.
  38. Praveenkumar T., Saimurugan M., Krishnakumar P., Ramachandran K.I., 2014. Fault diagnosis of
    automobile gearbox based on machine learning techniques. 12th Global Congress on
    Manufacturing and Management (GCMM), pp.2092-2098. December 08-10, Vellore (India).
  39. Fumeo E., Oneto L., Anguita D., 2015. Condition based maintenance in railway transportation system based on Big Data streaming analysis. INN Conference on Big Data, pp. 437-446. August 08-10, San Francisco (California, USA).
  40. Ali S.M., Hui K.H., Hee L.M., Salman Leong M., Al-Obaidi M.A., Ali Y.H., Abdelrhman A.M., 2018. A comparative experimental study on the use of machine learning approaches for automated valve monitoring based on acoustic emission parameters. 3rd International Conference on Mechanical, Manufacturing and Process Plant Engineering (ICMMPE), UNSP 012032. November 22-23, Penang (Malaysia).
  41. Li X., Wei L., He J., 2018. Design and implementation of equipment maintenance predictive model based on machine learning. 2nd annual international conference on cloud technology and communication engineering, UNSP 012001. August 17-19, Nanjing (China).
  42. Koltsidopoulos P.A., Dawood T., Thies P.R., 2018. Data insights from an offshore wind turbine
    gearbox replacement. EERA DeepWind'2018,15th Deep Sea Offshore Wind R&D Conference, art. no. 012003. January 17-19, Trondheim (Norway).
  43. Elshrif M., Rizzo S.G., Betz F.D., Margineantu D.D., Zaki M.J., Chawla S., 2018. Embeddings for the Identification of Aircraft Faults (MERIT). 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), art. no. 8448927. June 11-13, Seattle (Washington, USA).
  44. Zanelli D., López E., Pavez C., Pedreros J., Jain J., Avaria G., Moreno J., Bora B., Davis S., Soto L.,
    2018. Analysis of deterioration in a plasma focus device. XX Chilean Physics Symposium,
    November 30-December 2, Santiago (Chile).
  45. Katona A., Panfilov P., 2018. Building predictive maintenance framework for smart environment
    application systems. 29th International DAAM Symposium “Intelligent Manufacturing & Automation”, October 24-27, Zadar (Croatia).
  46. Morales F.J., Reyes A., Cáceres N., Romero L.M., Benitez F.G., Morgado J., Duarte E., Martins T.,
    2017. Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach. Building up Efficient and Sustainable Transport Infrastructure 2017 (BESTInfra2017), September 21-11, Prague (Czech Republic).
  47. Sun C., Zhang Z., He Z., 2011. Research on bearing life prediction based on support vector machine and its application. 9th International conference on demage assessment of structure (DAMAS), July 11-13, Oxford (UK).
  48. Mobley R. K., 2002. An Introduction to Predictive Maintenance. 2nd ed. Butterworth-Heineman.