86.8 Rezumat, Referințe

Ingineria de refabricare a demonstrat un impact pozitiv asupra durabilității energetice, protecției mediului și dezvoltării economice globale. Teoria și tehnologia refabricării s-au dezvoltat rapid, iar piața produselor refabricate continuă să crească la nivel mondial. Durata de viață utilă rămasă (RUL) a utilajelor aflate în funcțiune este un factor determinant direct al refabricării acestora. Alegerea timpului de retragere pentru refabricare nu este derivată numai din RUL, ci și dintr-o echilibrare cuprinzătoare a tuturor efectelor privind tehnologia, economia și impactul asupra mediului. Această abordare holistică optimizează procesul de refabricare.

Refabricarea este un proces complex de sistem. Teoria refabricării oferă suport pentru următoarele etape ale procesului: (1) determinarea „refabricării” unei piese vechi; (2) echilibrare și judecată cuprinzătoare pentru proiectarea planificării procesului de refabricare (RPP); (3) și evaluarea calității și fiabilității sau a duratei de viață a piesei refabricate. O evaluare exactă și eficientă a RUL va avea ca rezultat o evaluare rezonabilă și corectă a refabricării.

Modelarea RUL poate fi clasificată ca bazată pe model fizic și bazată pe model antrenat pe date. Principalele metode de prognoză RUL sunt support vector machine (SVM) și modelul state-space (SSM), așa cum a fost ilustrat în studiile de caz. Acuratețea evaluării RUL în modelul bazat pe date este afectată nu numai de modelul în sine, ci și de factori multipli, cum ar fi achiziția de date, procesarea semnalului și erorile de măsurare. În căutarea unui model mai exact pentru evaluarea RUL, se recomandă un proiect robust de sistem de achiziție de date pentru a reduce eficiența zgomotului în modelarea RUL.

Referințe

Akaike H (1975) Markovian representation of stochastic process by canonical variables. SIAM J Contr 13(1):162–172. doi:10.1137/0313010

Bie ZH, Wang XF (1997) The application of Monte Carlo method to reliability evaluation of power systems. Automat Electr Power Syst 6:68–75

Bryan D (2005) The remanufacturing revolution. Proceedings of international workshop on sustainable manufacturing. Shanghai China 10:95–101

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.2009.2038170

Chapelle VN (2002) Choosing multiple parameters for support vector machines. Mach Learn 46 (1–3):131–159. doi:10.1023/A:1012450327387

Chen XH (2007) Theory and algorithms on state space modeling and its applications in financial econometrics. Dissertation, Ji’nan University

Chen G, Zhou J (2008) Research on parameters and forecasting interval of support vector regression model to small sample. ACTA Metrologica Sinica 29(1):92–96

Chinnam RB, Baruah P (2005) HMMs for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275–1293. doi:10.1080/00207540412331327727

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Royal Soc B 39:1–38

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.2000.877920

Fletcher R (1987) Practical methods of optimization, 2nd edn. Wiley, New York

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/16.10.906

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)00024-5

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.2004.20

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-X

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:10.1243/0954408001530146

Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/nonGaussian Bayesian state estimation. IEEE Proc F 140(2):107–113

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)00046-4

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:10.1186/2210-4690-3-3

Hauser W, Lund R (2008) Remanufacturing: operating practices and strategies. Boston University, Boston

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:10.1007/1153908778

Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/cjlin/

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.2005.09.012

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, 113:7–20

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.2013.06.004

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.2006.1677362

Lund R (2008) Remanufacturing: operating practices and strategies. Boston University, Boston, MA

Lund RT (1996) The Remanufacturing industry-hidden giant. Research report, Manufacturing Engineering Department, Boston University

Mehra RK (1979) Kalman filters and their applications to forecasting. TIMS Stud Manag Sci :75–94

National Natural Science Foundation of Engineering and Materials Science Department (2010) Mechanical engineering disciplines development strategy report (2011–2020). Science Press, Beijing, pp 107–133

Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. Proc SPIE 4733(1):122–132. doi:10.1117/12.475502

Orchard M, Wu BQ, Vachtsevanos G (2005) A particle filtering framework for failure prognosis. Proceedings of WTC2005 World Tribology Congress, 883–884

Paris PC, Gomez RE, Anderson WE (1961) A rational analytic theory of fatigue. Trend Eng 13:9–14

PHM Society (2010) PHM data challenge, In: https://www.phmsociety.org/competition/phm/10

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:10.1080/09544820802406129

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:10.1109/87.508893

Steinhilper R, Zhu S, Yao JK (2006) Remanufacturing–the best form of recycling. National Defense Industry Press, Beijing

Steinwart I, Christmann A (2008) Support vector machines. Springer. New York. doi:10.1007/978-0-387-77242-4

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:10.1109/78.978383

Stribeck R (1907) Reports from the Central Laboratory for scientific technical investigation. ASME Trans 29:420–466

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.2012.02.016

The US Department of Energy Office of Industrial Technologies (1996) Remanufacturing vision statement. National Academy Press, Washington, DC

Vapnik VN (1998) Statistical learning theory. Wiley, New York. doi:10.1109/72.788640

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/WR022i004p00587

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.2003.09.003

Wohler A (1867) Wohler’s experiments on the strength of metals. Engineering 2:160–161

Xu B (2006) Binshi Xu talk remanufacturing. China Mach Electr Ind 6:40–41

Xu BS (2010) Remanufacturing both at home and abroad and future trend of development of the new. J Mech Eng Guide 4:15–19

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, Beijing

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/s11771-005-0002-4

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.2007.05.006

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:10.1080/09537280412331309208

Yang XH, Ma JS, Li N (1998) The fatigue limit and life prediction of steam turbine blade material. J Mech Strength 20(4):247–249

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.2006.01.021

Zhang F (1997) Damage limitation and life prediction of wheel steel. J Mech Strength 19(4):52–55

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.2005.1582757