Abstract
Over the last decade, a rapid development of internet, wireless mobile telecommunication, and product identification technologies make whole product life cycle visible and controllable, which can improve several operational issues over the whole product life cycle: product design improvement, predictive maintenance, rational decision on end-of-life products, and so on. The key element to solve these issues is to assess the degradation status of a product based on gathered data during product usage period. However, despite its importance, due to the interrupted information flow of the product life cycle after product sales, it has not received enough attention in the literature until now. To overcome this limitation, this study develops a decision support method, called degradation mode and criticality analysis (DMCA), for the analysis of product degradation status based on gathered product usage data. The proposed method enables us to identify and assess the degradation status of a product and give a suitable guide for the next action. To show the effectiveness of the proposed approach, a case study for a heavy construction equipment vehicle is introduced.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
PROMISE project commission (2004) PROMISE—integrated project: annex I—description of work. Project proposal
Thomas V, Neckel W, Wagner S (1999) Information technology and product lifecycle management. Proc IEEE International Symposium on Electronics and Environment pp. 54−57
Middendorf A, Griese H, Grimm WM, Reichl H (2003) Embedded life-cycle information module for monitoring and identification of product use conditions. Proc EcoDesign 2003 pp. 733−739
Chao MT (1999) Degradation analysis and related topics: some thoughts and a review. Proc Natl Sci Counc ROC (A) 23(5):555–566
Lu CJ, Meeker WQ, Escobar LA (1996) A comparison of degradation and failure-time analysis methods for estimating a time-to-failure distribution. Stat Sin 6:531–546
Lee J (1996) Measurement of machine performance degradation using a neural network model. Comput Ind 30(3):193–209
Xu H, Kwan CM, Haynes L, Pryor JD (1997) Machine performance degradation monitoring using fuzzy CMAC. Proc the American Control Conference pp. 1363−1364
Huang YS (2001) A decision model for deteriorating repairable systems. IIE Trans 33:479–485
Djurdjanovic D, Lee J, Ni J (2003) Watchdog agent-an infotronics-based prognostics approach for product performance degradation assessment and prediction. Adv Eng Inform 17(3–4):109–125
Frangopol DM, Kallen MJ, van Noortwijk JM (2004) Probabilistic models for life-cycle performance of deteriorating structures: review and future directions. Prog Struct Eng Mater 6:197–212
Wang P, Coit DW (2004) Reliability prediction based on degradation modeling for systems with multiple degradation measures. Proc RAMS pp. 302−307
Kara S, Mazhar M, Kaebernick H, Ahmed A (2005) Determining the reuse potential of components based on life cycle data. Ann CIRP 54(1):1–4
Kmenta S (2000) A method for predicting and evaluating failures in products and processes, Ph.D. Dissertation. Stanford University
Koç M, Lee J (2001) A system framework for next-generation E-maintenance systems. Technical report, Center for Intelligent Maintenance Systems. Univ. of Wisconsin-Milwaukee
Bluvband Z, Grabov P, Nakar O (2004) Expanded FMEA (EFMEA). Proc RAMS 2004 pp. 31−36
Lee BH (1999) Design FMEA for mechatronic systems using bayesian network causal models. Proc ASME 1999, Las Vegas, USA pp. 1235−1246
Umeda Y, Ishill M, Shimomura Y, Tomiyama T (1996) Supporting conceptual design based on the function-behaviour-state modeler. Artif Intell Eng Des Anal Manuf 10(4):275–288
Stamatis DH (2003) Failure mode effect analysis: FMEA from theory to execution. American Society for Quality Press
Cattaneo C (2006) Development of a new approach for product design improvement, considering middle-of-life (MOL) data. Master thesis, Dept. of Mechanical Engineering, EPFL, Switzerland
Dowling NE (1999) Mechanical behavior of materials. Second Edition, Prentice Hall
Monahan CC (1995) Early fatigue crack growth at welds. Computational Mechanics Publications, Southampton UK and Boston USA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shin, JH., Jun, HB., Catteneo, C. et al. Degradation mode and criticality analysis based on product usage data. Int J Adv Manuf Technol 78, 1727–1742 (2015). https://doi.org/10.1007/s00170-014-6782-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-014-6782-7