Skip to main content

Advertisement

SpringerLink
Log in

Maintenance and remanufacturing strategy: using sensors to predict the status of wind turbines

  • Research
  • Published:
Journal of Remanufacturing Aims and scope Submit manuscript

Abstract

The costs associated with inspecting wind turbines are high due to their size and complexity. One potential method by which such costs can be reduced, is through the development of robust systems that can monitor the conditions of wind turbines remotely. This study proposes embedding sensors into wind turbines to monitor the conditions of the wind turbines throughout their life cycles. The information retrieved from these sensors could be helpful in two ways: It could facilitate the provision of predictive maintenance for the turbines and enhance the performance of end-of-life (EOL) processing operations. During the maintenance phase, sensors can help to predict failures before they occur because they provide condition information about the products. During the EOL processing phase, they help to improve disassembly and inspection operations. Therefore, the use of embedded sensors in wind turbines could potentially reduce maintenance costs and increase EOL profit. This study compares regular and sensor-embedded wind turbine systems, which are modeled using discrete event simulation. A design of experiments study was carried out on the models. During the experimental stage, while conducting experiments, key variables, such as maintenance cost, disassembly cost, inspection cost, and EOL profit, were monitored. At the analysis stage, pairwise t-tests were performed to determine the statistical significance of the results. The results stage revealed that sensors can provide significant benefits to closed-loop supply chain systems when they are embedded into wind turbines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Alshamrani A, Mathur K, Ballou RH (2007) Reverse logistics: simultaneous design of delivery routes and returns strategies. Comput Oper Res 34:595–619

    Article  MATH  Google Scholar 

  2. Blanc HM, Fleuren HA, Krikke HR (2004) Redesign of a recycling system for LPG-tanks. OR Spectr 26:283–304

    Article  MATH  Google Scholar 

  3. Blanc LL, Krieken MV, Krikke H, Fleuren H (2006) Vehicle routing concepts in the closed-loop container network of ARN – a case study. OR Spectr 28:52–70

    Article  MATH  Google Scholar 

  4. Brander P, Forsberg R (2005) Cyclic lot scheduling with sequence-dependent set-ups: a heuristic for disassembly processes. Int J Prod Res 43(2):295–310

    Article  MATH  Google Scholar 

  5. Chang CY, Hung SS (2012) Implementing RFIC and sensor technology to measure temperature and humidity inside concrete structures. Constr Build Mater 26(1):628–637

    Article  Google Scholar 

  6. Cheng FT, Huang GW, Chen CH, Hung MH (2004) A generic embedded device for retrieving and transmitting information of various customized applications. In: Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on (vol 1, pp 978–983), IEEE

  7. Dahane M, Sahnoun M, Bettayeb B, Baudry D, Boudhar H (2017) Impact of spare parts remanufacturing on the operation and maintenance performance of offshore wind turbines: a multi-agent approach. J Intell Manuf 28(7):1531–1549

    Article  Google Scholar 

  8. De Faria H, Costa JGS, Olivas JLM (2015) A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renew Sust Energ Rev 46:201–209

    Article  Google Scholar 

  9. Dethloff J (2001) Vehicle routing and reverse logistics: the vehicle routing problem with simultaneous delivery and pick-up. OR-Spektrum 23(1):79–96

    Article  MathSciNet  MATH  Google Scholar 

  10. Dethloff J (2002) Relation between vehicle routing problems: an insertion heuristic for the vehicle routing problem with simultaneous delivery and pick-up applied to the vehicle routing problem with backhauls. J Oper Res Soc 53(1):115–118

    Article  MATH  Google Scholar 

  11. Dulman MT, Gupta SM (2015a) Use of sensors for cell phones. Proceedings for the Production and Operations Management Society (POMS)

  12. Dulman MT, Gupta SM (2015b) Disassembling and remanufacturing end-of-life sensor embedded cell phones. Innovation and Supply Chain Management 9(4):111–117

    Article  Google Scholar 

  13. Dulman MT, Gupta SM (2016) Use of sensors for collection of end-of-life products. Proceedings for the Northeast Region Decision Sciences Institute (NEDSI)

  14. Dulman MT, Gupta SM (2018) Evaluation of maintenance and EOL operation performance of sensor-embedded laptops. Logistics 2(1), Paper No. 3:1–22

    Google Scholar 

  15. Efthymiou K, Papakostas N, Mourtzis D, Chryssolouris G (2012) On a predictive maintenance platform for production systems. Procedia CIRP 3:221–226

    Article  Google Scholar 

  16. Govindan K, Bouzon M (2018) From a literature review to a multi-perspective framework for reverse logistics barriers and drivers. J Clean Prod 187:318–337

    Article  Google Scholar 

  17. Gribkovskaia I, Halskau Ø, Laporte G, Vlček M (2007) General solutions to the single vehicle routing problem with pickups and deliveries. Eur J Oper Res 180(2):568–584

    Article  MathSciNet  MATH  Google Scholar 

  18. Gungor A, Gupta SM (1999) Issues in environmentally conscious manufacturing and product recovery: a survey. Comput Ind Eng 36(4):811–853

    Article  Google Scholar 

  19. Gupta SM (2013) Reverse supply chains: issues and analysis. CRC Press, Boca Raton ISBN: 978-1439899021

    Book  Google Scholar 

  20. Gupta SM, Ilgin MA (2018) Multiple criteria decision making applications in environmentally conscious manufacturing and product recovery. CRC Press, Boca Raton ISBN: 978-1498700658

    Google Scholar 

  21. Gupta SM, Taleb KN (1994) Scheduling disassembly. Int J Prod Res V32(8):1857–1866

    Article  MATH  Google Scholar 

  22. Hatcher GD, Ijomah WL, Windmill JFC (2011) Design for remanufacture: a literature review and future research needs. J Clean Prod 19(17–18):2004–2014

    Article  Google Scholar 

  23. Ilgin MA, Gupta SM (2010a) Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art. J Environ Manag 91(3):563–591

    Article  Google Scholar 

  24. Ilgin MA, Gupta SM (2010b) Comparison of economic benefits of sensor embedded products and conventional products in a multi-product disassembly line. Comput Ind Eng 59(4):748–763

    Article  Google Scholar 

  25. Ilgin MA, Gupta SM (2011a) Evaluating the impact of sensor-embedded products on the performance of an air conditioner disassembly line. Int J Adv Manuf Technol 53(9–12):1199–1216

    Article  Google Scholar 

  26. Ilgin MA, Gupta SM (2011b) Performance improvement potential of sensor embedded products in environmental supply chains. Resour Conserv Recycl 55(6):580–592

    Article  Google Scholar 

  27. Ilgin MA, Gupta SM (2011c) Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line. Robot Comput Integr Manuf 27(2):318–334

    Article  Google Scholar 

  28. Ilgin MA, Gupta SM (2012) Remanufacturing modeling and analysis. CRC Press, Boca Raton ISBN: 9781439863077

    Book  Google Scholar 

  29. Ilgin MA, Gupta SM, Nakashima K (2011) Coping with disassembly yield uncertainty in remanufacturing using sensor embedded products. Journal of Remanufacturing 1(1):1–14

    Article  Google Scholar 

  30. Ilgin MA, Ondemir O, Gupta SM (2014) An approach to quantify the financial benefit of embedding sensors into products for end-of-life management: a case study. Prod Plan Control 25(1):26–43

    Article  Google Scholar 

  31. Ilgin MA, Gupta SM, Battaïa O (2015) Use of MCDM techniques in environmentally conscious manufacturing and product recovery: state of the art. J Manuf Syst 37:746–758

    Article  Google Scholar 

  32. Kelton DW, Sadowski RP, Sadowski DA (2007) Simulation with arena, 4th edn. McGraw-Hill, New York

    Google Scholar 

  33. Kim HJ, Xirouchakis P (2010) Capacitated disassembly scheduling with random demand. Int J Prod Res 48(23):7177–7194

    Article  MATH  Google Scholar 

  34. Kim HJ, Lee DH, Xirouchakis P, Züst R (2003) Disassembly scheduling with multiple product types. CIRP Annals-Manufacturing Technology 52(1):403–406

    Article  Google Scholar 

  35. Kim HJ, Lee DH, Xirouchakis P (2006) Two-phase heuristic for disassembly scheduling with multiple product types and parts commonality. Int J Prod Res 44(1):195–212

    Article  Google Scholar 

  36. Kim H, Yang J, Lee KD (2009a) Vehicle routing in reverse logistics for recycling end-of-life consumer electronic goods in South Korea. Transp Res Part D: Transp Environ 14(5):291–299

    Article  Google Scholar 

  37. Kim HJ, Lee DH, Xirouchakis P, Kwon OK (2009b) A branch and bound algorithm for disassembly scheduling with assembly product structure&star. J Oper Res Soc 60(3):419–430

    Article  MATH  Google Scholar 

  38. Krikke H, le Blanc I, van Krieken M, Fleuren H (2008) Low-frequency collection of materials disassembled from end-of-life vehicles: on the value of on-line monitoring in optimizing route planning. Int J Prod Econ 111(2):209–228

    Article  Google Scholar 

  39. Lee DH, Kim HJ, Choi G, Xirouchakis P (2004) Disassembly scheduling: integer programming models. Proc Inst Mech Eng B J Eng Manuf 218(10):1357–1372

    Article  Google Scholar 

  40. McFarlane D, Sarma S, Chirn JL, Wong C, Ashton K (2003) Auto ID systems and intelligent manufacturing control. Eng Appl Artif Intell 16(4):365–376

    Article  Google Scholar 

  41. Meyer GG, Främling K, Holmström J (2009) Intelligent products: a survey. Comput Ind 60(3):137–148

    Article  Google Scholar 

  42. Mourão MC, Almeida MT (2000) Lower-bounding and heuristic methods for a refuse collection vehicle routing problem. Eur J Oper Res 121(2):420–434

    Article  MATH  Google Scholar 

  43. Mourão MC, Amado L (2005) Heuristic method for a mixed capacitated arc routing problem: a refuse collection application. Eur J Oper Res 160(1):139–153

    Article  MATH  Google Scholar 

  44. Ondemir O, Gupta SM (2012) Optimal management of reverse supply chains with sensor-embedded end-of-life products. In: Lawrence KD, Kleinman G (eds) Applications of management science, vol 15. Emerald Group Publishing Limited, Bingley, p 109–129

  45. Ondemir O, Gupta SM (2013a) Advanced remanufacturing-to-order and disassembly-to-order system under demand/decision uncertainty. In: Gupta SM (ed) Reverse supply chains: Issues and analysis. CRC Press, Florida, p 203–228

  46. Ondemir O, Gupta SM (2013b) Quality assurance in remanufacturing with sensor embedded products. In: Nikolaidis Y (ed) Quality Management in Reverse Logistics. Springer-Verlag, London, pp 95–112

  47. Ondemir O, Gupta SM (2014a) A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system. Eur J Oper Res 233(2):408–419

    Article  MATH  Google Scholar 

  48. Ondemir O, Gupta SM (2014b) Quality management in product recovery using the internet of things: an optimization approach. Comput Ind 65(3):491–504

    Article  Google Scholar 

  49. Ondemir O, Ilgin MA, Gupta SM (2012) Optimal end-of-life management in closed-loop supply chains using RFID and sensors. Industrial Informatics, IEEE Transactions on 8(3):719–728

    Article  Google Scholar 

  50. Ortegon K, Nies LF, Sutherland JW (2013) Preparing for end of service life of wind turbines. J Clean Prod 39:191–199

    Article  Google Scholar 

  51. Ortegon-Mosquera K (2014) Value characterization across the life cycle: A model to support value recovery from used wind turbines. PhD Thesis, (Advisors: Dr. John W. Sutherland and Dr. Loring F. Nies), Ecological Sciences and Engineering, Purdue University, West Lafayette, Indiana

  52. Phadke MS (1989) Quality engineering robust design. Prentice Hall, New Jersey

    Google Scholar 

  53. Prakash PKS, Ceglarek D, Tiwari MK (2012) Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem. Int J Adv Manuf Technol 60(9–12):1125–1137

    Article  Google Scholar 

  54. Priyono A, Ijomah W, Bititci U (2016) Disassembly for remanufacturing: a systematic literature review, new model development and future research needs. Journal of Industrial Engineering and Management 9(4):899–932

    Article  Google Scholar 

  55. Rios PJ, Stuart JA (2004) Scheduling selective disassembly for plastics recovery in an electronics recycling center. Electronics Packaging Manufacturing, IEEE Transactions on 27(3):187–197

    Article  Google Scholar 

  56. Rodriguez JP, Perez CR (2002) Advanced sensor for optimal orientation and predictive maintenance of high power wind generators. In: IECON 02 [Industrial Electronics Society, IEEE 2002 28th Annual Conference of the], (vol 3, pp 2167–2172), IEEE

  57. Schmidt A, Van Laerhoven K (2001) How to build smart appliances? Personal Communications, IEEE 8(4):66–71

    Article  Google Scholar 

  58. Schultmann F, Zumkeller M, Rentz O (2006) Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry. Eur J Oper Res 171(3):1033–1050

    Article  MATH  Google Scholar 

  59. Stuart JA, Christina V (2003) New metrics and scheduling rules for disassembly and bulk recycling. Electronics Packaging Manufacturing, IEEE Transactions on 26(2):133–140

    Article  Google Scholar 

  60. Tian X, Zhang Z-H (2018) Capacitated disassembly scheduling and pricing of returned products with price-dependent yield. Omega. https://doi.org/10.1016/j.omega.2018.04.010

  61. Vadde S, Kamarthi S, Gupta SM, Zeid I (2008) Product life cycle monitoring via embedded sensors. In: Gupta SM, Lambert AJD (eds) Environment conscious manufacturing. CRC Press, Boca Raton, p 91–103

    Google Scholar 

  62. Zhang Y, Liu S, Liu Y, Yang H, Li M, Huisingh D, Wang L (2018) The ‘internet of things’ enabled real-time scheduling for remanufacturing of automobile engines. J Clean Prod 185:562–575

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, College of Engineering, Northeastern University, Boston, MA, 02115, USA

    Mehmet Talha Dulman & Surendra M. Gupta

Authors
  1. Mehmet Talha Dulman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surendra M. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surendra M. Gupta.

Appendix

Appendix

Table 14 LQ Remanufactured wind turbine and subassembly prices
Full size table
Table 15 LQ Remanufactured wind turbine and subassembly demands
Full size table
Table 16 Maintenance data
Full size table
Table 17 Subassembly replacement times for maintenance
Full size table
Table 18 Subassembly failure probabilities (%)
Full size table
Table 19 Production and cost data
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dulman, M.T., Gupta, S.M. Maintenance and remanufacturing strategy: using sensors to predict the status of wind turbines. Jnl Remanufactur 8, 131–152 (2018). https://doi.org/10.1007/s13243-018-0050-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13243-018-0050-1

Keywords

Search

Navigation