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Use of Embedded Smart Sensors in Products to Facilitate Remanufacturing

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Abstract

Unlike in traditional manufacturing, remanufacturers face uncertainty in quality, quantity, and frequency of returned products, making the remanufacturing processes less predictable and remanufacturing decision-making more difficult. The research on the use of embedded smart sensors in products to facilitate remanufacturing through monitoring and registering information associated with the products, e.g., their state-of-health, remaining service life, remanufacturing history, etc., has received increasingly high level of interests. This chapter first introduces the background of sensor-embedded products, including the essential parts of a typical smart sensor and product information model. Next, the current practices toward the development of embedded smart sensors in products are reviewed in detail in two aspects, namely, (1) embedding smart sensors in products and (2) representing and interpreting sensor data. A conceptual framework is presented to illustrate how sensor data gathered using smart sensors can be managed to facilitate product remanufacturing decision-making. Lastly, future research trends are given to address the challenges efficiently in using embedded smart sensors for facilitating remanufacturing processes and planning.

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References

  • Al-Habaibeh A, Gindy N (2000) New approach for systematic design of condition monitoring systems for milling processes. J Mater Process Technol 107(1–3):243–251

    Article  Google Scholar 

  • Al-Habaibeh A, Parkin RM, Redgate J (2005) The design of enhanced condition monitoring systems using a novel sensor positioning and signal conditioning approach. In: Proceedings of the 20th biennial conference on mechanical vibration and noise, Long Beach, 24–28 Sept 2005, pp 661–668

    Google Scholar 

  • Alkhadafe H, Al-Habaibeh A, Daihzong S, Lotfi A (2012) Optimising sensor location for an enhanced gearbox condition monitoring system. 25th international congress on condition monitoring and diagnostic engineering. J Phys Conf Ser 364:012077

    Article  Google Scholar 

  • Aras N, Boyaci T, Verter V (2004) The effect of categorizing returned products in remanufacturing. IIE Trans 36(4):319–331

    Article  Google Scholar 

  • Borguet S, Leonard O (2008) The fisher information matrix as a relevant tool for sensor selection in engine health monitoring. Int J Rotat Mach 2008:784749, 10 p

    Article  Google Scholar 

  • Bras B (2008) Design for remanufacturing processes. In: Kutz M (ed) Environmentally conscious mechanical design. Wiley, Hoboken, pp 283–318

    Google Scholar 

  • Bras B, McIntosh MW (1999) Product, process and organizational design for remanufacture – an overview of research. Robotic Comput Integr Manuf 15(3):167–178

    Article  Google Scholar 

  • Chen ZS, Yang YM, Zheng H (2012) A technical framework and roadmap of embedded diagnostics and prognostics for complex mechanical systems in prognostics and health management systems. IEEE Trans Reliab 61(2):314–322

    Article  Google Scholar 

  • Cheng S, Azarian M, Pecht M (2008) Sensor system selection for prognostics and health monitoring. In: Proceedings of the ASME 2008 international design engineering technical conferences & computers and information in engineering conference, Brooklyn, 3–6 Aug 2008, pp 1383–1389

    Google Scholar 

  • Cheng S, Tom K, Thomas L, Pecht M (2010) A wireless sensor system for prognostics and health management. IEEE Sensor J 10(4):856–862

    Article  Google Scholar 

  • Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent – an infotronics-based prognostics approach for product performance degradation assessment and prediction. AdvEng Inform 17(3–4):109–125

    Google Scholar 

  • Du R, Elbestawi MA, Wu SM (1995a) Automated monitoring of manufacturing processes, part I – monitoring methods. ASME J Eng Ind 117(2):121–132

    Article  Google Scholar 

  • Du R, Elbestawi MA, Wu SM (1995b) Automated monitoring of manufacturing processes, part II – applications. ASME J Eng Ind 117(2):133–141

    Article  Google Scholar 

  • Engel SJ, Gilmartin BJ, Bongort K, Hess A (2000) Prognostics, the real issues involved with predicting life remaining. In: Proceedings of 2000 I.E. aerospace cConference, Big Sky, 18–25 Mar 2000, pp 457–469

    Google Scholar 

  • Ferrer G, Heath SK, Dew N (2011) An RFID application in large job shop remanufacturing operations. Int J Prod Econ 133(2):612–621

    Article  Google Scholar 

  • Fleming F (2001) Overview of automotive sensors. IEEE Sensor J 1(4):296–308

    Article  MathSciNet  Google Scholar 

  • Fleming F (2008) New automotive sensors – a review. IEEE Sensor J 8(11):1900–1921

    Article  MathSciNet  Google Scholar 

  • Galbreth MR, Blackburn JD (2010) Optimal acquisition quantities in remanufacturing with condition uncertainty. Prod Oper Manag 19(1):61–69

    Article  Google Scholar 

  • Geyer R, Van Wassenhove LN, Atasu A (2007) The economics of remanufacturing under limited component durability and finite product life cycles. Manag Sci 53(1):88–100

    Article  MATH  Google Scholar 

  • Goebel K, Eklund N, Bonanni P (2006) Fusing competing prediction algorithms for prognostics. In: Proceedings of 2006 I.E. aerospace conference, Big Sky, 4–11 Mar 2006, pp 1–10

    Google Scholar 

  • Goebel K, Saha B, Saxena A (2008) A comparison of three data-driven techniques for prognostics. In: 62nd meeting of the society for machinery failure prevention technology, Virginia Beach, VA, 6–8 May 2008, pp 119–131

    Google Scholar 

  • Harrison M (2003) EPC™ information service – data model and queries. CAM-AUTOID-WH-025, Institute for Manufacturing, University of Cambridge. Available at: http://www.autoidlabs.org/uploads/media/CAM-AUTOID-WH025.pdf. last accessed on 1 April 2014

  • Heng A, Zhang S, Tan CC, Mathew J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process 23(3):724–739

    Article  Google Scholar 

  • Hribernik KA, von Stietencron M, Hans C, Thoben KD (2011) Intelligent products to support closed-loop reverse logistics, In: Hesselbach J, Herrmann C (eds) Globalized solutions for sustainability in manufacturing. Proceedings of the 18th CIRP conference on life cycle engineering, Technische Universität Braunschweig, Braunschweig, 2–4 May 2011, pp 486–491

    Google Scholar 

  • Ilgin MA, Gupta SM (2010) 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 

  • Ilgin MA, Gupta SM, Nakashima K (2011) Coping with disassembly yield uncertainty in remanufacturing using sensor embedded products. J Remanuf 1:7

    Article  Google Scholar 

  • Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Article  Google Scholar 

  • Joshi S, Boyd S (2009) Sensor selection via convex optimization. IEEE Trans Signal Process 57(2):451–462

    Article  MathSciNet  Google Scholar 

  • Kaehernick H, Kara S (2008) Reuse and recycling technologies. In: Kutz M (ed) Environmentally conscious mechanical design. Wiley, Hoboken, pp 249–282

    Google Scholar 

  • Kalyan-Seshu US, Bras B (1997) Integrating I-DEAS with remanufacturing and assemblability assessments. In: Proceedings of the 1997 ASME design automation conference, ASME design technical conferences & computers in engineering conference, Sacramento, 14–17 Sept 1997, pp 1–11

    Google Scholar 

  • Kara S (2010) Assessing remaining useful lifetime of products. In: Cochran JJ (ed) Wiley encyclopedia of operations research and management science. Wiley, Hoboken

    Google Scholar 

  • Kara S, Mazhar MI, Kaebernick H, Ahmed H (2005) Determining the reuse potential of components based on life cycle data. Ann CIRP 54(1):1–4

    Article  Google Scholar 

  • Kara S, Manmek S, Kaebernick H, Ibbotson S (2008) Assessment of products for optimal lifetime. CIRP Ann Manuf Technol 57(1):1–4

    Article  Google Scholar 

  • Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multi-sensor data fusion: a review of the state-of-the-art. Inform Fus 14(1):28–44

    Article  Google Scholar 

  • Kiritsis D, Bufardi A, Xirouchakis P (2003) Research issues on product lifecycle management and information tracking using smart embedded systems. Adv Eng Inform 17(3–4):189–202

    Article  Google Scholar 

  • Klausner M, Grimm WM, Hendrickson C, Horvath A (1998) Sensor-based data recording of use conditions for product takeback. In: Proceedings of the 1998 I.E. international symposium on electronics and the environment, Chicago, 4–6 May 1998, pp 138–143

    Google Scholar 

  • Kulkarni A, Ralph D, McFarlane D (2007) Value of RFID in remanufacturing. Int J Serv Oper Inform 2(3):225–252

    Google Scholar 

  • Kumar S, Dolev E, Pecht M (2010) Parameter selection for health monitoring of electronic products. Microelectron Reliab 50(2):161–168

    Article  Google Scholar 

  • Lee J, Ni J, Djurdjanovic D, Qiu H, Liao H (2006) Intelligent prognostics tools and e-maintenance. Comput Ind 57(6):476–489

    Article  Google Scholar 

  • Liang Y, Pokharel S, Lim GH (2009) Pricing used products for remanufacturing. Eur J Oper Res 193(2):390–395

    Article  MATH  MathSciNet  Google Scholar 

  • Mushini R, Simon D (2005) On optimization of sensor selection for aircraft gas turbine engines. In: Proceedings of 18th international conference on the systems engineering, Las Vegas, 16–18 Aug 2005, pp 8–14

    Google Scholar 

  • Niu G, Yang BS, Pecht M (2010) Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance. Reliab Eng Syst Safe 95(7):786–796

    Article  Google Scholar 

  • Ondemir O, Gupta SM (2013) A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system. Eur J Oper Res 233(2):408–419

    Google Scholar 

  • Parlikad AK, McFarlane D (2007) RFID-based product information in end-of-life decision making. Contr Eng Pract 15(11):1348–1363

    Article  Google Scholar 

  • Parlikad AK, McFarlane D (2010) Quantifying the impact of AIDC technologies for vehicle component recovery. Comput Ind Eng 59(2):296–307

    Article  Google Scholar 

  • Pecht M (2008) Prognostics and health management of electronics. Wiley, Hoboken

    Book  Google Scholar 

  • Pourali M, Mosleh A (2012) A functional sensor placement optimization method for power systems health monitoring. In: Proceedings of the 2012 I.E. industry applications society annual meeting. Las Vegas, 7–11 Oct 2012, pp 1–10

    Google Scholar 

  • Roemer MJ, Byington CS, Kacprzynski GJ (2006) An overview of selected prognostic technologies with application to engine health management. In: Proceedings of GT2006 ASME turbo expo 2006: power for land, sea, and air, Barcelona, 8–11 May 2006, pp 707–715

    Google Scholar 

  • Rostad CC, Myklebust O, Mosenget B (2005) Closing the product life cycle information loops. In: Proceedings of the 18th international conference on production research, 31 July–2 Aug 2005, University of Salerno, Fisciano

    Google Scholar 

  • Santi L, Sowers T, Aguilar R (2005) Optimal sensor selection for health monitoring systems. In: Proceedings of the 41st joint propulsion conference and exhibit, the Tucson, 10–13 July 2005

    Google Scholar 

  • Saxena A, Celaya J, Balaban E, Goebel K, Saha B, Saha S, Schwabacher M (2008) Metrics for evaluating performance of prognostic techniques. In: Proceedings of the 2008 international conference on prognostics and health management, Denver, 6–9 Oct 2008, pp 1–17

    Google Scholar 

  • Saxena A, Celaya J, Balaban E, Saha B, Saha S, Goebel K (2009) Evaluating algorithm performance metrics tailored for prognostics. In: Proceedings of the 2009 I.E. Aerospace conference, Big Sky, 9–14 Mar 2009, pp 1–13

    Google Scholar 

  • Schwabacher M (2005) A survey of data-driven prognostics. In: Proceedings of the AIAA Infotech@Aerospace Conference, Arlington, VA, 26–29 Sept 2005, pp 1–5

    Google Scholar 

  • Schwabacher M, Goebel K (2007) A survey of artificial intelligence for prognostics. In: The AAAI Fall Symposium on Artificial Intelligence for Prognostics, Arlington, VA, 9–11 Nov 2007, pp 107–114

    Google Scholar 

  • Schweinstig S (2010) Cleaning engineering – efficient cleaning in remanufacturing. ReMaTecNews 10(6):20–27

    Google Scholar 

  • Seliger G, Buchholz A, Kross U (2003) Enhanced product functionality with life cycle units. Proc Inst Mech Eng Part [B] 217(9):1197–1202

    Article  Google Scholar 

  • Sheng SW, Zhang L, Gao RX (2006) A systematic sensor-placement strategy for enhanced defect detection in rolling bearings. IEEE Sensor J 6(5):1346–1354

    Google Scholar 

  • Si X-S, Wang W-B, Hua CH, Zhou DH (2011) Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14

    Article  Google Scholar 

  • Sikorska JZ, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836

    Article  Google Scholar 

  • Simon M, Bee G, Moore P, Pu J-S, Xie C (2001) Modelling of the life cycle of products with data acquisition features. Comput Ind 45(2):111–122

    Article  Google Scholar 

  • Smith VM, Keoleian GA (2004) The value of remanufacturing of engine – lifecycle environmental and economic perspective. J Ind Ecol 8(1–2):193–221

    Google Scholar 

  • Spencer BF Jr, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: opportunities and challenges. Struct Contr Health Monit 11(4):349–368

    Article  Google Scholar 

  • Steinhilper R (1998) Remanufacturing – the ultimate form of recycling. Fraunhofer IRB, Stuttgart

    Google Scholar 

  • Subrahmanya N, Shin YC, Meckl PH (2010) A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics. Mech Syst Signal Process 24(1):182–192

    Article  Google Scholar 

  • 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, pp 91–103

    Google Scholar 

  • Vichare N, Pecht M (2006) Prognostics and health management of electronics. IEEE Trans Compon Pack T 29(1):222–229

    Google Scholar 

  • Wang YF, Ma XD, Malcolm J (2012) Optimal sensor selection for wind turbine condition monitoring using multivariate principal component analysis approach. In: Proceedings of the 18th international conference on automation & computing, Leicestershire, 7–8 Sept 2012, pp 1–7

    Google Scholar 

  • Yang XY, Moore P, Chong SK (2009) Intelligent products: from lifecycle data acquisition to enabling product-related services. Comput Ind 60(3):184–194

    Article  Google Scholar 

  • Yuan ML, Ong SK, Nee AYC (2008) Augmented reality for assembly guidance using a virtual interactive tool. Int J Prod Econ 46(7):1745–1767

    Article  MATH  Google Scholar 

  • Zeid A, Kamarthi S, Gupta SM (2004) Product take-back: sensors-based approach. In: Gupta SM (eds) Environmentally conscious manufacturing IV. Proceedings of SPIE, vol 5583, Bellingham, pp 200–206.

    Google Scholar 

  • Zhang GF, Vachtsevanos G (2007) A methodology for optimum sensor localization/selection in fault diagnosis. In: Proceedings of IEEE aerospace conference, Big Sky, 3–10 Mar 2007, pp 1–8

    Google Scholar 

  • Zhang J, Ong SK, Nee AYC (2011) RFID-assisted assembly guidance system in an augmented reality environment. Int J Prod Econ 49(13):3919–3938

    Article  Google Scholar 

  • Zikopoulos C, Tagaras G (2007) Impact of uncertainty in the quality of returns on the profitability of a single-period refurbishing operation. Eur J Oper Res 182(1):205–225

    Article  MATH  MathSciNet  Google Scholar 

  • Zikopoulos C, Tagaras G (2008) On the attractiveness of sorting before disassembly in remanufacturing. IIE Trans 40(3):313–323

    Article  Google Scholar 

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Authors and Affiliations

  1. Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, 117576, Singapore, Singapore

    H. C. Fang, S. K. Ong & A. Y. C. Nee

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  1. H. C. Fang
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  2. S. K. Ong
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  3. A. Y. C. Nee
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Correspondence to H. C. Fang .

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Editors and Affiliations

  1. Faculty of Engineering, Mechanical Engineering Department, National University of Singapore, Singapore, Singapore

    Andrew Y. C. Nee

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Fang, H.C., Ong, S.K., Nee, A.Y.C. (2015). Use of Embedded Smart Sensors in Products to Facilitate Remanufacturing. In: Nee, A. (eds) Handbook of Manufacturing Engineering and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-4670-4_85

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