A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery

  • Sara AntomarioniEmail author
  • Ornella Pisacane
  • Domenico Potena
  • Maurizio Bevilacqua
  • Filippo Emanuele Ciarapica
  • Claudia Diamantini


Effective maintenance policies can support companies to deal with process interruptions and consequently, to prevent significant profit losses. Moreover, the proliferation of structured and unstructured data due to production plants validates the application of knowledge discovery in databases techniques to increase processes’ reliability. In this paper, an innovative maintenance policy is proposed. It aims at both predicting components breakages through association rule mining and determining the optimal set of components to repair in order to improve the overall plant’s reliability, under time and budget constraints. An experimental campaign is carried out on a real-life case study concerning an oil refinery plant. Finally, numerical results are discussed considering different blockage categories and number of components and by varying some significant input parameters.


Predictive maintenance Association rules Integer linear programming Knowledge discovery in databases Industry 4.0 



  1. 1.
    Accorsi R, Bortolini M, Gamberi M, Manzini R, Pilati F (2017) Multi-objective warehouse building design to optimize the cycle time, total cost, and carbon footprint. Int J Adv Manuf Tech 92(1–4):839–854. CrossRefGoogle Scholar
  2. 2.
    Agard B, Kusiak A (2004) Data mining for subassembly selection. J Manuf Sci E-T ASME 126(3):627–631. CrossRefzbMATHGoogle Scholar
  3. 3.
    Alkhamis TM, Yellen J (1995) Refinery units maintenance scheduling using integer programming. Appl Math Model 19(9):543–549. CrossRefzbMATHGoogle Scholar
  4. 4.
    Allaoui H, Artiba A (2004) Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints. Comput Ind Eng 47(4):431–450. CrossRefGoogle Scholar
  5. 5.
    Alrabghi A, Tiwari A (2015) State of the art in simulation-based optimisation for maintenance systems. Comput Ind Eng 82:167–182. CrossRefGoogle Scholar
  6. 6.
    Antomarioni S, Bevilacqua M, Potena D, Diamantini C (2019) Defining a data-driven maintenance policy an application to an oil refinery plant. Int J Qual Reliab Manag. CrossRefGoogle Scholar
  7. 7.
    Bertolini M, Bevilacqua M (2006) A combined goal programming-AHP approach to maintenance selection problem. Reliab Eng Syst Safe 91(7):839–848. CrossRefGoogle Scholar
  8. 8.
    Bevilacqua M, Ciarapica FE (2018) Human factor risk management in the process industry: a case study. Reliab Eng Syst Safe 169:149–159. CrossRefGoogle Scholar
  9. 9.
    Bortolini M, Ferrari E, Gamberi M, Pilati F, Faccio M (2017) Assembly system design in the industry 4.0 era: a general framework. IFAC-PapersOnLine 50(1):5700–5705. CrossRefGoogle Scholar
  10. 10.
    Bortolini M, Gamberi M, Mora C, Pilati F, Regattieri A (2017) Design, prototyping, and assessment of a wastewater closed-loop recovery and purification system. Sustainability 9(11):1938. CrossRefGoogle Scholar
  11. 11.
    Buddhakulsomsiri J, Siradeghyan Y, Zakarian A, Li X (2006) Association rule-generation algorithm for mining automotive warranty data. Int J Prod Res 44(14):2749–2770. CrossRefzbMATHGoogle Scholar
  12. 12.
    Cassady CR, Bowden RO, Liew L, Pohl EA (2000) Combining preventive maintenance and statistical process control: a preliminary investigation. Iie Trans 32(6):471–478. CrossRefGoogle Scholar
  13. 13.
    Chalabi N, Dahane M, Beldjilali B, Neki A (2016) Optimisation of preventive maintenance grouping strategy for multi-component series systems: particle swarm based approach. Comput Ind Eng 102:440–451. CrossRefGoogle Scholar
  14. 14.
    Chen MC (2003) Configuration of cellular manufacturing systems using association rule induction. Int J Prod Res 41(2):381–395. CrossRefzbMATHGoogle Scholar
  15. 15.
    Chen WC, Tseng SS, Wang CY (2004) A novel manufacturing defect detection method using data mining approach. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, pp 77–86. CrossRefGoogle Scholar
  16. 16.
    Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20(5):501. CrossRefGoogle Scholar
  17. 17.
    Da Cunha C, Agard B, Kusiak A (2006) Data mining for improvement of product quality. Int J Prod Res 44(18-19):4027–4041. CrossRefzbMATHGoogle Scholar
  18. 18.
    Dekker R (1996) Applications of maintenance optimization models: a review and analysis. Reliab Eng Syst Safe 51(3):229–240. CrossRefGoogle Scholar
  19. 19.
    Diamantini C, Potena D, Storti E (2013) A virtual mart for knowledge discovery in databases. Inform Syst Front 15(3):447–463. CrossRefGoogle Scholar
  20. 20.
    Ding SH, Kamaruddin S (2015) Maintenance policy optimization-literature review and directions. Int J Adv Manuf Tech 76(5–8):1263–1283. CrossRefGoogle Scholar
  21. 21.
    Djatna T, Alitu IM (2015) An application of association rule mining in total productive maintenance strategy: an analysis and modelling in wooden door manufacturing industry. Procedia Manufacturing 4:336–343. CrossRefGoogle Scholar
  22. 22.
    Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI mag 17(3):37. CrossRefGoogle Scholar
  23. 23.
    Gharbi A, Kenné JP (2005) Maintenance scheduling and production control of multiple-machine manufacturing systems. Comput Ind Eng 48(4):693–707. CrossRefGoogle Scholar
  24. 24.
    Goel HD, Grievink J, Weijnen MP (2003) Integrated optimal reliable design, production, and maintenance planning for multipurpose process plants. Comput Chem Eng 27(11):1543–1555 . CrossRefGoogle Scholar
  25. 25.
    Hadjaissa B, Ameur K, Essounbouli N, et al. (2016) Bi-objective optimization of maintenance scheduling for power systems. Int J Adv Manuf Tech 85(5-8):1361–1372. CrossRefGoogle Scholar
  26. 26.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. SIGMOD Rec 29 (2):1–12. CrossRefGoogle Scholar
  27. 27.
    Harding J, Shahbaz M, Kusiak A, et al. (2006) Data mining in manufacturing: a review. J Manuf Sci E-T ASME 128(4):969–976. CrossRefGoogle Scholar
  28. 28.
    Ilgin MA, Tunali S (2007) Joint optimization of spare parts inventory and maintenance policies using genetic algorithms. Int J Adv Manuf Tech 34(5–6):594–604. CrossRefGoogle Scholar
  29. 29.
    Irawan CA, Ouelhadj D, Jones D, Stålhane M, Sperstad IB (2017) Optimisation of maintenance routing and scheduling for offshore wind farms. Eur J Oper Res 256(1):76–89. MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Kamsu-Foguem B, Rigal F, Mauget F (2013) Mining association rules for the quality improvement of the production process. Expert Syst Appl 40 (4):1034–1045. CrossRefGoogle Scholar
  31. 31.
    Kenné JP, Boukas E, Gharbi A (2003) Control of production and corrective maintenance rates in a multiple-machine, multiple-product manufacturing system. Math Comput Model 38(3–4):351–365. MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Laggoune R, Chateauneuf A, Aissani D (2010) Impact of few failure data on the opportunistic replacement policy for multi-component systems. Reliab Eng Syst Safe 95(2):108–119. CrossRefGoogle Scholar
  33. 33.
    Lee HT, Yang DL, Yang SJ (2013) Multi-machine scheduling with deterioration effects and maintenance activities for minimizing the total earliness and tardiness costs. Int J Adv Manuf Tech 66(1-4):547–554. CrossRefGoogle Scholar
  34. 34.
    Marseguerra M, Zio E, Podofillini L (2002) Condition-based maintenance optimization by means of genetic algorithms and monte carlo simulation. Reliab Eng Syst Safe 77(2):151–165. CrossRefGoogle Scholar
  35. 35.
    Mokhtari H, Mozdgir A, Kamal Abadi IN (2012) A reliability/availability approach to joint production and maintenance scheduling with multiple preventive maintenance services. Int J Prod Res 50(20):5906–5925. CrossRefGoogle Scholar
  36. 36.
    Nedic N, Prsic D, Dubonjic L, Stojanovic V, Djordjevic V (2014) Optimal cascade hydraulic control for a parallel robot platform by PSO. Int Journal Adv Manuf Tech 72 (5-8):1085–1098. CrossRefGoogle Scholar
  37. 37.
    Nedic N, Stojanovic V, Djordjevic V (2015) Optimal control of hydraulically driven parallel robot platform based on firefly algorithm. Nonlinear Dynam 82(3):1457–1473. MathSciNetCrossRefGoogle Scholar
  38. 38.
    Nedić N, Pršić D, Fragassa C, Stojanović V, Pavlovic A (2017) Simulation of hydraulic check valve for forestry equipment. Int J Heavy Veh Syst 24(3):260–276. CrossRefGoogle Scholar
  39. 39.
    Olafsson S, Li X, Wu S (2008) Operations research and data mining. Eur J Oper Res 187(3):1429–1448. MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Pardalos PM, Hansen P (2008) Data mining and mathematical programming, vol 45. American Mathematical Soc. CrossRefGoogle Scholar
  41. 41.
    Martínez-de Pisón FJ, Sanz A, Martínez-de Pisón E, Jiménez E, Conti D (2012) Mining association rules from time series to explain failures in a hot-dip galvanizing steel line. Comput Ind Eng 63(1):22–36. CrossRefGoogle Scholar
  42. 42.
    Pistikopoulos EN, Vassiliadis CG, Papageorgiou LG (2000) Process design for maintainability: an optimization approach. Comput Chem Eng 24(2–7):203–208. CrossRefGoogle Scholar
  43. 43.
    Rezg N, Xie X, Mati Y (2004) Joint optimization of preventive maintenance and inventory control in a production line using simulation. Int J Prod Res 42(10):2029–2046. CrossRefzbMATHGoogle Scholar
  44. 44.
    Rygielski C, Wang JC, Yen DC (2002) Data mining techniques for customer relationship management. Technol Soc 24(4):483–502. CrossRefGoogle Scholar
  45. 45.
    Sarker R, Haque A (2000) Optimization of maintenance and spare provisioning policy using simulation. Appl Math Model 24 (10):751–760. CrossRefzbMATHGoogle Scholar
  46. 46.
    Shafiee M (2017) Maintenance optimization and inspection planning of wind energy assets: models, methods and strategies. Reliab Eng Syst Safe. CrossRefGoogle Scholar
  47. 47.
    Nedic N (2016) A nature inspired parameter tuning approach to cascade control for hydraulically driven parallel robot platform. J Optimiz Theory App 168(1):332–347. MathSciNetCrossRefzbMATHGoogle Scholar
  48. 48.
    Stojanovic V, Nedic N, Prsic D, Dubonjic L, Djordjevic V (2016) Application of cuckoo search algorithm to constrained control problem of a parallel robot platform. Int J Adv Manuf Tech 87(9–12):2497–2507. CrossRefGoogle Scholar
  49. 49.
    Tagaras G (1988) An integrated cost model for the joint optimization of process control and maintenance. J Oper Res Soc 39(8):757–766. CrossRefzbMATHGoogle Scholar
  50. 50.
    Vilarinho S, Lopes I, Oliveira JA (2017) Preventive maintenance decisions through maintenance optimization models: a case study. Procedia Manufacturing 11:1170–1177. CrossRefGoogle Scholar
  51. 51.
    Wang K (2007) Applying data mining to manufacturing: the nature and implications. J Intell Manuf 18 (4):487–495. CrossRefGoogle Scholar
  52. 52.
    Wang S, Liu M (2015) Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning. J Manuf Syst 37:182–192. CrossRefGoogle Scholar
  53. 53.
    Wang Z, Shao X, Zhang G, Zhu H (2005) Integration of variable precision rough set and fuzzy clustering: an application to knowledge acquisition for manufacturing process planning. In: International workshop on rough sets, fuzzy sets, data mining, and granular-soft computing. Springer, pp 585–593Google Scholar
  54. 54.
    Xia T, Xi L, Lee J, Zhou X (2011) Optimal CBPM policy considering maintenance effects and environmental condition. Int J Adv Manuf Tech 56(9–12):1181–1193. CrossRefGoogle Scholar
  55. 55.
    Xia T, Xi L, Zhou X, Lee J (2013) Condition-based maintenance for intelligent monitored series system with independent machine failure modes. Int J Prod Res 51(15):4585–4596. CrossRefGoogle Scholar
  56. 56.
    Zhang S, Wu X (2011) Fundamentals of association rules in data mining and knowledge discovery. Wires Data Min Knowl 1(2):97–116. MathSciNetCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Sara Antomarioni
    • 1
    Email author
  • Ornella Pisacane
    • 2
  • Domenico Potena
    • 2
  • Maurizio Bevilacqua
    • 1
  • Filippo Emanuele Ciarapica
    • 1
  • Claudia Diamantini
    • 2
  1. 1.Dipartimento di Ingegneria Industriale e Scienze MatematicheUniversitá Politecnica delle MarcheAnconaItaly
  2. 2.Dipartimento di Ingegneria dell’InformazioneUniversitá Politecnica delle MarcheAnconaItaly

Personalised recommendations