Data-driven CBM tool for risk-informed decision-making in an electric arc furnace

  • Luca Fumagalli
  • Laura CattaneoEmail author
  • Irene Roda
  • Marco Macchi
  • Maurizio Rondi


Nowadays, maintenance activities and safety management can be supported by a mature state of the art favouring the implementation of condition-based maintenance programme, which recommends maintenance decisions based on the information collected through asset life. The main idea, which grounds in the Industry 4.0 paradigm, is to utilize the asset degradation information, extracted and identified through different techniques, to reduce and eliminate costly, unscheduled downtimes and unexpected breakdowns and to avoid risky scenarios. This paper aims at developing and testing a data-driven CBM tool to provide fault diagnostics transforming raw data from the shop-floor into information, finally enabling risk-informed decision-making. The tool relies on a process of knowledge discovery that incorporates both prior knowledge and proper interpretation of data analytics results. Prior knowledge is extracted through a process hazard analysis (PHA), while data analysis deals with statistical process control and novelty detection. The model is proposed to integrate some Cyber-Physical System element in the extant plant automation, to exploit its computational capabilities through the continuous monitoring and data analytics. This enables a “watchdog agent” of risky scenario, allowing an on-line risk-assessment of safety-critical components, finally enhancing the intelligence in the industrial process.


Condition-based maintenance Process hazard analysis Statistical process control Novelty detection Decision support system Risk assessment 



  1. 1.
    Lee J, Jin C, Liu Z, Davari Ardakani H (2017) Introduction to data-driven methodologies for prognostics and health management. In: Ekwaro-Osire S, Gonçalves AAF (eds) Probabilistic Prognostics and Health Management of Energy Systems. Springer, Cham, pp 9–32CrossRefGoogle Scholar
  2. 2.
    Shrouf F, Ordieres J, Miragliotta G (2014, 2015) Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. IEEE Int Conf Ind Eng Eng Manag:697–701.
  3. 3.
    Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manuf 11:939–948. CrossRefGoogle Scholar
  4. 4.
    Sarma S, Ashton K, Brock D (1999) The networked physical world. Technical Report MIT-AUTOID-WH-001.
  5. 5.
    Ashton K (2009) That “internet of things” thing. RFiD J 4986:17–19. Google Scholar
  6. 6.
    Baheti R, Gill H (2011) Cyber-physical systems. Impact Control Technol:161–166.
  7. 7.
    Jazdi N (2014) Cyber physical systems in the context of Industry 4.0. 2014, IEEE autom Qual testing. Robot:2–4.
  8. 8.
    Lee J, Bagheri B, Kao HA (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23. CrossRefGoogle Scholar
  9. 9.
    Kusiak A (2006) Data mining: manufacturing and service applications. Int J Prod Res 44:4175–4191. CrossRefzbMATHGoogle Scholar
  10. 10.
    Kuo Y-H, Kusiak A (2018) From data to big data in production research: the past and future trends. Int J Prod Res 7543:1–26. Google Scholar
  11. 11.
    Cattaneo L, Fumagalli L, Macchi M, Negri E (2018) Clarifying data analytics concepts for industrial engineering. IFAC-PapersOnLine 51:820–825. CrossRefGoogle Scholar
  12. 12.
    Ge Z, Song Z, Ding SX, Huang B (2017) Data mining and analytics in the process industry: the role of machine learning. IEEE Access 5:20590–20616. CrossRefGoogle Scholar
  13. 13.
    Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent - an infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv Eng Informatics 17:109–125. CrossRefGoogle Scholar
  14. 14.
    Colace C, Fumagalli L, Pala S, Macchi M, Matarazzo NR, Rondi M (2013) An intelligent maintenance system to improve safety of operations of an electric furnace in the steel making industry. Progn Health Manag Conf 33:397–402. Google Scholar
  15. 15.
    Colace C, Fumagalli L, Pala S, Macchi M, Matarazzo NR, Rondi M (2015) Implementation of a condition monitoring system on an electric arc furnace through a risk-based methodology. Proc Inst Mech Eng O J Risk Reliab 229:327–342. Google Scholar
  16. 16.
    Fumagalli L, Macchi M, Colace C, Rondi M, Alfieri A (2016) A smart maintenance tool for a safe electric arc furnace. IFAC-PapersOnLine 49:19–24. MathSciNetCrossRefGoogle Scholar
  17. 17.
    Heng A, Tan ACC, Mathew J, Montgomery N, Banjevic D, Jardine AKS (2009) Intelligent condition-based prediction of machinery reliability. Mech Syst Signal Process 23:1600–1614. CrossRefGoogle Scholar
  18. 18.
    Niu G, Yang B-S, Pecht M (2010) Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance. Reliab Eng Syst Saf 95:786–796. CrossRefGoogle Scholar
  19. 19.
    Arunraj NS, Maiti J (2010) Risk-based maintenance policy selection using AHP and goal programming. Saf Sci 48:238–247. CrossRefGoogle Scholar
  20. 20.
    Marseguerra M, Zio E, Podofillini L (2002) Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliab Eng Syst Saf 77:151–165. CrossRefGoogle Scholar
  21. 21.
    Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510. CrossRefGoogle Scholar
  22. 22.
    Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostics tools and e-maintenance. Comput Ind 57:476–489. CrossRefGoogle Scholar
  23. 23.
    Deloux E, Castanier B, Bérenguer C (2008) Maintenance policy for a deteriorating system evolving in a stressful environment. Proc Inst Mech Eng O J Risk Reliab 222:613–622. Google Scholar
  24. 24.
    Huynh KT, Barros A, Bérenguer C (2012) Adaptive condition-based maintenance decision framework for deteriorating systems operating under variable environment and uncertain condition monitoring. Proc Inst Mech Eng O J Risk Reliab 226:602–623. Google Scholar
  25. 25.
    Guillén AJ, Crespo A, Macchi M, Gómez J (2016) On the role of prognostics and health management in advanced maintenance systems. Prod Plan Control 27:991–1004. CrossRefGoogle Scholar
  26. 26.
    Bengtsson M (2004) Standardization issues in condition based maintenance. Proc 16th Int Congr 53Google Scholar
  27. 27.
    ISO/IEC (2003) ISO 13374 - 1 - Condition monitoring and diagnostics of machines -- Data processing, communication and presentation -- Part 1: General guidelinesGoogle Scholar
  28. 28.
    Lei Y, Li N, Guo L, Li N, Yan T, Lin J (2018) Machinery health prognostics: a systematic review from data acquisition to RUL prediction. Mech Syst Signal Process 104:799–834. CrossRefGoogle Scholar
  29. 29.
    Dragomir OE, Gouriveau R, Dragomir F, Minca E, Zerhouni N (2018) Review of prognostic problem in condition-based maintenance. 2009 Eur Control Conf 1587–1592.
  30. 30.
    Fugate ML, Sohn H, Farrar CR (2001) Vibration-based damage detection using statistical process control. Mech Syst Signal Process 15:707–721. CrossRefGoogle Scholar
  31. 31.
    Skormin VA, Popyack LJ, Gorodetski VI, Araiza ML, Michel JD (2008) Applications of cluster analysis in diagnostics-related problems vol 3, pp 161–168.
  32. 32.
  33. 33.
    Goumas SK, Zervakis ME, Stavrakakis GS (2002) Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction. IEEE Trans Instrum Meas 51:497–508. CrossRefGoogle Scholar
  34. 34.
    Jain AK, Duin RPW, Mao J (2000) Statistical pattern Recognition: a review. IEEE Trans Pattern Anal Mach Intell 22:4–37CrossRefGoogle Scholar
  35. 35.
    Sun Q, Chen P, Zhang D, Xi F (2004) Pattern Recognition for automatic machinery fault diagnosis. J Vib Acoust 126:307. CrossRefGoogle Scholar
  36. 36.
    Mechefske CK, Mathew J (1992) Fault detection and diagnosis in low speed rolling element bearings part II: the use of nearest neighbour classification. Mech Syst Signal Process 6:309–316. CrossRefGoogle Scholar
  37. 37.
    Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning. Springer series in statistics. Springer, New YorkGoogle Scholar
  38. 38.
    Accorsi R, Manzini R, Pascarella P, Patella M, Sassi S (2017) Data mining and machine learning for condition-based maintenance. Procedia Manuf 11:1153–1161. CrossRefGoogle Scholar
  39. 39.
    Poyhonen S, Jover P, Hyotyniemi H (2004) Signal processing of vibrations for condition monitoring of an induction motor. 499–502.
  40. 40.
    Qingfeng W, Wenbin L, Xin Z, Jianfeng Y, Qingbin Y (2011) Development and application of equipment maintenance and safety integrity management system. J Loss Prev Process Ind 24:321–332. CrossRefGoogle Scholar
  41. 41.
    Pimentel MAF, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215–249. CrossRefGoogle Scholar
  42. 42.
    Cubillo A, Perinpanayagam S, Esperon-Miguez M (2016) A review of physics-based models in prognostics: application to gears and bearings of rotating machinery. Adv Mech Eng 8:1–21. CrossRefGoogle Scholar
  43. 43.
    Ferro L, Giugliano P, Galbiati P, Memoli, FGC, Maiolo J (2007) The electric arc furnace of Tenaris Dalmine: from the application of the new technologies of digital electrode regulation and multipoint injection to the dynamic control of the process. In: METEC, METEC International Metallurgy Trade Fair with Congress, 2007, InSteelCon, International Steel Conference on new Developments in Metallurgical Process Technologies, 3. Stahlinstitut VDEh, pp 504–512Google Scholar
  44. 44.
    Paalanen P, Kamarainen J-K, Ilonen J, Kälviäinen H (2006) Feature representation and discrimination based on Gaussian mixture model probability densities—practices and algorithms. Pattern Recogn 39:1346–1358. CrossRefzbMATHGoogle Scholar
  45. 45.
    Filev DP, Tseng F (2006) Real time novelty detection modeling for machine health prognostics. Annu Conf North Am Fuzzy Inf Process Soc - NAFIPS pp 529–534.
  46. 46.
    Markou M, Singh S (2003) Novelty detection: a review—part 1: statistical approaches. Signal Process 83:2481–2497. CrossRefzbMATHGoogle Scholar
  47. 47.
    Fumagalli L, Macchi M, Rapaccini M (2009) Computerized maintenance management systems in SMEs: a survey in Italy and some remarks for the implementation of condition based maintenance. IFACGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Management, Economics and Industrial EngineeringPolitecnico di MilanoMilanItaly
  2. 2.Tenaris Dalmine S.p.A.DalmineItaly

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