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Remanufacturing and Remaining Useful Life Assessment

  • Hong-chao Zhang
  • Shujie Liu
  • Huitian Lu
  • Yuanliang Zhang
  • Yawei Hu
Reference work entry

Abstract

The remanufacturing of machinery is a process of disassembly, where parts are inspected, repaired, reconditioned or replaced, recertified, and then reassembled to “like-new” condition. In the modern manufacturing industry, remanufacturing offers the advantages of sustainable energy development, cost savings, and pollution reduction, among other benefits. Assessment of the remaining useful life (RUL) of machinery is key for remanufacturing to determining what components can be shut down and when for off-line remanufacturing. Hence, the accuracy of RUL online assessment is critical to remanufacturing industry practice.

This chapter first discusses the concept and technology of remanufacturing and then summarizes the modeling of online RUL assessment, with both physical and data-driven models, comparing the advantages and disadvantages of each technique. Also discussed are two new specific methods in remaining life assessment widely cited in the literature: support vector machine (SVM) method and state-space method (SSM). Two case studies of machine tooling are illustrated in detail, using examples and data from real industry applications to demonstrate RUL assessment. The chapter ends by raising, deliberating, and dispelling some practical concerns with online sensing data modeling and RUL assessment.

Keywords

Support Vector Machine Fatigue Life Support Vector Regression Particle Filter Acoustic Emission Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Akaike H (1975) Markovian representation of stochastic process by canonical variables. SIAM J Contr 13(1):162–172. doi:10.1137/0313010CrossRefMATHMathSciNetGoogle Scholar
  2. Bie ZH, Wang XF (1997) The application of Monte Carlo method to reliability evaluation of power systems. Automat Electr Power Syst 6:68–75Google Scholar
  3. Bryan D (2005) The remanufacturing revolution. Proceedings of international workshop on sustainable manufacturing. Shanghai China 10:95–101Google Scholar
  4. Camci F, Chinnam RB (2010) Health-state estimation and prognostics in machining processes [J]. IEEE Trans Automat Sci Eng 7(3):581–597. doi:10.1109/TASE.2009.2038170CrossRefGoogle Scholar
  5. Chapelle VN (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1–3):131–159. doi:10.1023/A:1012450327387CrossRefMATHGoogle Scholar
  6. Chen XH (2007) Theory and algorithms on state space modeling and its applications in financial econometrics. Dissertation, Ji’nan UniversityGoogle Scholar
  7. Chen G, Zhou J (2008) Research on parameters and forecasting interval of support vector regression model to small sample. ACTA Metrologica Sinica 29(1):92–96Google Scholar
  8. Chinnam RB, Baruah P (2005) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275–1293. doi:10.1080/00207540412331327727CrossRefMATHGoogle Scholar
  9. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Soc B 39:1–38MATHMathSciNetGoogle Scholar
  10. Engel SJ, Gilmartin BJ, Bongort K et al (2000) Prognostics, the real issues involved predicting life remaining. IEEE Aerospace Conf Proc 6:457–470. doi:10.1109/AERO.2000.877920Google Scholar
  11. Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  12. Furey TS, Cristianini N, Duffy N et al (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906–914. doi:10.1093/bioinformatics/16.10.906CrossRefGoogle Scholar
  13. Glodez S, Sraml M, Kramberger J (2002) A computational model for determination of service life of gears. Int J Fatigue 24(10):1013–1020. doi:10.1016/S0142-1123(02)00024-5CrossRefMATHGoogle Scholar
  14. Gokhale SS, Mullen RE (2004) From test count to code coverage using the lognormal failure rate. In: 15th international symposium on software reliability engineering. Connecticut Univ., Storrs, CT, USA, pp 295–305. doi: 10.1109/ISSRE.2004.20Google Scholar
  15. Goldhor RS, Robert TL (1983) University-to-industry advanced technology transfer: a case study. Res Pol 12(3):121–152. doi:10.1016/0048-7333(83)90015-XCrossRefGoogle Scholar
  16. Goode KB, Moore J, Roylance BJ (2000) Plant machinery working life prediction method utilizing reliability and condition-monitoring data. Proc Inst Mech Eng 214(2):109–122. doi:10.1243/0954408001530146CrossRefGoogle Scholar
  17. Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/nonGaussian Bayesian state estimation. IEEE Proc F 140(2):107–113Google Scholar
  18. Guo G, Li SZ, Chan KL (2001) Support vector machines for face recognition. Image Vis Comput 19(9):631–638. doi:10.1016/S0262-8856(01)00046-4CrossRefGoogle Scholar
  19. Hatcher GD, Ijomah WL, Windmill JF (2013) Design for remanufacturing in China: a case study of electrical and electronic equipment. J Remanufactur 3(1):1–11. doi:10.1186/2210-4690-3-3CrossRefGoogle Scholar
  20. Hauser W, Lund R (2008) Remanufacturing: operating practices and strategies. Boston University, BostonGoogle Scholar
  21. Hong W, Chen C et al (2005) Recurrent support vector machines in reliability prediction. In: Advances in natural computation. LNCS Springer, Changsha, pp 619–629. doi:10.1007/1153908778Google Scholar
  22. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/
  23. Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Proc 20(7):1483–1510. doi:10.1016/j.ymssp.2005.09.012CrossRefGoogle Scholar
  24. Jin G, David EM, Zhou HB (2013). A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries in spacecraft. Reliability Engineering and System Safety, 113:7–20CrossRefGoogle Scholar
  25. Lee J, Fangji W, Zhao W et al (2014) Prognostics and health management design for rotary machinery systems – reviews, methodology and applications. Mech Syst Signal Proc 42:314–334. doi:10.1016/j.ymssp.2013.06.004CrossRefGoogle Scholar
  26. Liao HT, Zhao WB, Guo HR (2006) Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model. Reliab Maintain Symp, NewportBeach, CA:127–132. doi:10.1109/RAMS.2006.1677362Google Scholar
  27. Lund R (2008) Remanufacturing: operating practices and strategies. Boston University, Boston, MAGoogle Scholar
  28. Lund RT (1996) The Remanufacturing industry-hidden giant. Research report, Manufacturing Engineering Department, Boston UniversityGoogle Scholar
  29. Mehra RK (1979) Kalman filters and their applications to forecasting. TIMS Stud Manag Sci :75–94Google Scholar
  30. National Natural Science Foundation of Engineering and Materials Science Department (2010) Mechanical engineering disciplines development strategy report (2011–2020). Science Press, Beijing, pp 107–133Google Scholar
  31. Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. Proc SPIE 4733(1):122–132. doi:10.1117/12.475502Google Scholar
  32. Orchard M, Wu BQ, Vachtsevanos G (2005) A particle filtering framework for failure prognosis. Proceedings of WTC2005 World Tribology Congress, 883–884Google Scholar
  33. Paris PC, Gomez RE, Anderson WE (1961) A rational analytic theory of fatigue. Trend Eng 13:9–14Google Scholar
  34. PHM Society (2010) PHM data challenge, In: https://www.phmsociety.org/competition/phm/10
  35. Rao RV, Padmanabhan K (2010) Selection of best product end-of-life scenario using digraph and matrix methods. J Eng Des 21(4):455–472. doi:10.1080/09544820802406129CrossRefGoogle Scholar
  36. Ray A, Tangirala S (1996) Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Trans Contr Syst Technol 4(4):443–451. doi:10.1109/87.508893CrossRefGoogle Scholar
  37. Steinhilper R, Zhu S, Yao JK (2006) Remanufacturing–the best form of recycling. National Defense Industry Press, BeijingGoogle Scholar
  38. Steinwart I, Christmann A (2008) Support vector machines. Springer. New York. doi:10.1007/978-0-387-77242-4Google Scholar
  39. Storvik G (2002) Particle filters for state-space models with the presence of unknown static parameters. IEEE Trans Signal Proc 15(2):281–289. doi:10.1109/78.978383CrossRefMathSciNetGoogle Scholar
  40. Stribeck R (1907) Reports from the Central Laboratory for scientific technical investigation. ASME Trans 29:420–466Google Scholar
  41. Taboada J, Matias JM, Ordonez C, Nieto G (2007) Creating a quality map of a slate deposit using support vector machines. J Comput Appl Math 204:84–94. doi:10.1016/j.apm.2012.02.016CrossRefMATHGoogle Scholar
  42. The US Department of Energy Office of Industrial Technologies (1996) Remanufacturing vision statement. National Academy Press, Washington, DCGoogle Scholar
  43. Vapnik VN (1998) Statistical learning theory. Wiley, New York. doi:10.1109/72.788640Google Scholar
  44. Vogel RM (1986) The probability plot correlation coefficient test for the normal, lognormal and Gumbel distributional hypothesis. Water Resources Res 22(4):587–590. doi:10.1029/WR022i004p00587CrossRefGoogle Scholar
  45. Volk PJ, Wnek M, Zygmunt M (2004) Utilizing statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions. Mech Syst Signal Proc 18(4):833–847. doi:10.1016/j.ymssp.2003.09.003CrossRefGoogle Scholar
  46. Wohler A (1867) Wohler’s experiments on the strength of metals. Engineering 2:160–161Google Scholar
  47. Xu B (2006) Binshi Xu talk remanufacturing. China Mach Electr Ind 6:40–41Google Scholar
  48. Xu BS (2010) Remanufacturing both at home and abroad and future trend of development of the new. J Mech Eng Guide 4:15–19Google Scholar
  49. Xu BS, Zhang S (1999) Technical and theoretical research – remanufacturing engineering of 21-century modern manufacturing science. National Natural Science Foundation of China Mechanical Engineering: frontier and priority areas discussion anthology, BeijingGoogle Scholar
  50. Xu BS, Liu SC, Wang HD (2005) Developing remanufacturing engineering, constructing cycle economy and building saving-oriented society. J Cent South Univ Technol 12(2):1–6. doi:10.1007/s11771-005-0002-4CrossRefGoogle Scholar
  51. Xu GP, Tian WF, Li Q (2007) EMD and SVM based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG). Mech Syst Signal Proc 21(8):3182–3188. doi:10.1016/j.ymssp.2007.05.006CrossRefGoogle Scholar
  52. Yan J, Kog M, Lee J (2004) A prognostic algorithm for machine performance assessment and its application[J]. Prod Plann Contr 15(8):796–801. doi:10.1080/09537280412331309208CrossRefGoogle Scholar
  53. Yang XH, Ma JS, Li N (1998) The fatigue limit and life prediction of steam turbine blade material. J Mech Strength 20(4):247–249Google Scholar
  54. Yu PS, Chen ST, Chang IF (2006) Support vector regression for real-time flood stage forecasting. J Hydrol 328(3–4):704–716. doi:10.1016/j.jhydrol.2006.01.021CrossRefGoogle Scholar
  55. Zhang F (1997) Damage limitation and life prediction of wheel steel. J Mech Strength 19(4):52–55Google Scholar
  56. Zhang Q, Campillo F et al (2005) Nonlinear system fault detection and isolation based on bootstrap particle filters. In: Proceedings of the 44th IEEE conference on decision and control, and the European control conference 2005, Seville, pp 3821–3826. doi:10.1109/CDC.2005.1582757Google Scholar
  57. Zhang X, Zhang H, Jiang Z, Wang Y (2013) A decision-making approach for end-of-life strategies selection of used parts. Int J Adv Manuf Technol::1–8. doi:10.1007/s00170-013-5234-0Google Scholar
  58. Zhao Z, Huangb B, Liu F (2013) Parameter estimation in batch process using EM algorithm with particle filter. Comput Chem Eng 57(15):159–172CrossRefGoogle Scholar
  59. Zheng XY, Xie JL (1999) Probability failure accumulative method predicting fatigue life under spectrum loading. J Mech Strength 21(3):225–227Google Scholar
  60. Zheng XL, Lu BT, Cui TX et al (1994) Fatigue tests and life prediction of 16Mn steel butt weld without crack-like defect. Int J Fracture 68:275–280. doi:10.1007/BF00013072CrossRefGoogle Scholar
  61. Zhu S, Yao JK (2009) Remanufacturing design theory and application. Machinery Industry Press, BeijingGoogle Scholar
  62. Zhu S, Yao JK (2011) Re-manufacturing techniques and technology. Mechanical Industry Press, BeijingGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Hong-chao Zhang
    • 1
    • 2
  • Shujie Liu
    • 1
  • Huitian Lu
    • 1
    • 3
  • Yuanliang Zhang
    • 1
  • Yawei Hu
    • 1
  1. 1.School of Mechanical EngineeringDalian University of TechnologyDalianChina
  2. 2.Department of Industrial EngineeringTexas Tech UniversityLubbockUSA
  3. 3.Department of Engineering Technology and Management South Dakota State UniversityBrookingsUSA

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