Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T17:37:24.772Z Has data issue: false hasContentIssue false
  • English
  • Français

A maintenance-focused approach to complex system design

Published online by Cambridge University Press:  14 July 2016

Bo Yang Yu ,
Tomonori Honda ,
Syed M. Zubair ,
Mostafa H. Sharqawy  and
Maria C. Yang
Bo Yang Yu
Affiliation:
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Tomonori Honda
Affiliation:
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Syed M. Zubair
Affiliation:
Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Mostafa H. Sharqawy
Affiliation:
School of Engineering, University of Guelph, Guelph, Ontario, Canada
Maria C. Yang*
Affiliation:
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
*
Reprint requests to: Maria C. Yang, Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 3-449B, Cambridge, MA, USA. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Maintenance plays a critical role in reducing operating cost and maximizing reliability of a complex engineering system. This paper proposes a novel maintenance-focused, system-level design framework that attempts to capture the interactions between maintenance strategies and system-level design parameters overlooked in current modeling approaches. The goal of this maintenance-focused approach is to help designers better understand the interconnectedness of system architecture, choice of maintenance strategy, and uncertainties in a design. Application of the proposed design framework is demonstrated through a case example of a power plant condenser system. Results show that using an integrated approach can reveal the many nonobvious interactions between subsystems, and produce system designs that have lower life-cycle cost compared to traditional sequential design approaches.

Keywords

Design OptimizationMaintenanceResilienceSystem Engineering
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 3: System Architecture Design , August 2016 , pp. 263 - 276
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agte, J., de Weck, O., Sobieszczanski-Sobieski, J., Arendsen, P., Alan Morris, A., & Spieck, M. (2010). MDO: assessment and direction for advancement—an opinion of one international group. Structural and Multidisciplinary Optimization 40(1–6), 17–33.Google Scholar
Alfaris, A., Siddiqi, A., Charbel Rizk, C., & de Weck, O. (2010). Hierarchical decomposition and multidomain formulation for the design of complex sustainable systems. Journal of Mechanical Design 132(9), 091003.Google Scholar
Behdad, S., Kwak, M., Kim, H., & Thurston, D. (2010). Simultaneous selective disassembly and end-of-life decision making for multiple products that share disassembly operations. Journal of Mechanical Design 132(4), 041002.CrossRefGoogle Scholar
Bodden, D.S., Hadden, W., Grube, B.E., & Clements, N.S. (2005). PHM as a design variable in air vehicle conceptual design. Proc. IEEE Aerospace Conf., pp. 3384–3394, Big Sky, MT, March 5–12.CrossRefGoogle Scholar
Browning, T.R. (2001). Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Transactions on Engineering 48(3), 292–306.Google Scholar
Camci, F. (2009). System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability 58(3), 539–552.Google Scholar
Caputo, A.C., Pelagagge, P.M., & Salini, P. (2011). Joint economic optimization of heat exchanger design and maintenance policy. Applied Thermal Engineering 31(8–9), 1381–1392.Google Scholar
Dekker, R. (1996). Applications of maintenance optimization models: a review and analysis. Reliability Engineering & System Safety 51(3), 229–240.Google Scholar
Engel, S.J., Gilmartin, B.J., Bongort, K., & Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining. Proc. 2000 IEEE Aerospace Conf., Vol. 6, pp. 457–469, Big Sky, MT, March 18–25.Google Scholar
Eppinger, S.D., & Browning, T.R. (2012). Design Structure Matrix Methods and Applications. Cambridge, MA: MIT Press.Google Scholar
Eppinger, S.D., Joglekar, N.R., Olechowski, A., & Teo, T. (2014). Improving the systems engineering process with multilevel analysis of interactions. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(4), 323–337.Google Scholar
Epstein, N. (1983). Thinking about heat transfer fouling: a 5 × 5 matrix. Heat Transfer Engineering 4(1), 43–56.Google Scholar
Ghosh, S., Devendorf, E., & Lewis, K. (2014). Exploring the effectiveness of parallel systems in distributed design processes subjected to stochastic disruptions. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(4), 399–412.Google Scholar
Giffin, M., de Weck, O., Bounova, G., Keller, R., Eckert, C., & Clarkson, P.J. (2009). Change propagation analysis in complex technical systems. Journal of Mechanical Design 131(8), 081001.Google Scholar
Grall, A., Berenguer, C., & Dieulle, L. (2002). A condition-based maintenance policy for stochastically deteriorating systems. Reliability Engineering & System Safety 76(2), 167–180.Google Scholar
He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources 196(23), 10314–10321.Google Scholar
Holmgren, M. (2007). X Steam: Thermodynamic Properties of Water and Steam. Natick, MA: Mathworks.Google Scholar
Honda, T., & Antonsson, E.K. (2007). Coupling effects and sensitivity analysis for grayscale system reliability. Proc. ASME Int. Conf. Design Theory and Methodology, pp. 433–446, La Vegas, NV, September 4–7.Google Scholar
Hu, J., & Cardin, M.A. (2015). Generating flexibility in the design of engineering systems to enable better sustainability and life cycle performance. Research in Engineering Design 26(2), 121–143.Google Scholar
Jardine, A.K.S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 20(7), 1483–1510.CrossRefGoogle Scholar
Kakac, S. (1991). Boilers, Evaporators, and Condensers. New York: Wiley.Google Scholar
Kothamasu, R., Huang, S., & VerDuin, W. (2006). System health monitoring and prognostics—a review of current paradigms and practices. International Journal of Advanced Manufacturing Technology 28(9), 1012–1024.Google Scholar
Kurtoglu, T., & Tumer, I.Y. (2008). A graph-based fault identification and propagation framework for functional design of complex systems. Journal of Mechanical Design 130(5), 051401.Google Scholar
Leão, B.P., João, PP Gomes, Roberto, KH Galvão, & Yoneyama, T. (2010). How to tell the good from the bad in failure prognostics methods. Proc. 2010 IEEE Aerospace Conf., pp. 1–7. Manhattan Beach, CA: IEEE.Google Scholar
Lin, J., de Weck, O., de Neufville, R., Robinson, B., & MacGowan, D. (2009). Designing capital-intensive systems with architectural and operational flexibility using a screening model. Complex Sciences, Vol. 5, pp. 1935–1946. Berlin: Springer.Google Scholar
Liu, H., Chen, W., Kokkolaras, M., Papalambros, P.Y., & Kim, H.M. (2006). Probabilistic analytical target cascading: a moment matching formulation for multilevel optimization under uncertainty. Journal of Mechanical Design (128), 991.Google Scholar
Lokiec, F., & Kronenberg, G. (2001). Emerging role of BOOT desalination projects. Desalination 136(1), 109–114.CrossRefGoogle Scholar
Loyola, B.R., Zhao, Y., Loh, K.J., & La Saponara, V. (2013). The electrical response of carbon nanotube-based thin film sensors subjected to mechanical and environmental effects. Smart Materials and Structures 22, 025010.CrossRefGoogle Scholar
Lu, S., Schroeder, N.B., Kim, H.M., & Shanbhag, U.V. (2010). Hybrid power/energy generation through multidisciplinary and multilevel design optimization with complementarity constraints. Journal of Mechanical Design 132(10), 101007.CrossRefGoogle Scholar
Maillart, L.M., & Pollock, S.M. (2002). Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems. IEEE Transactions on Reliability 51(3), 322–330.Google Scholar
Martins, J.R.R.A., & Lambe, A.B. (2013). Multidisciplinary design optimization: a survey of architectures. AIAA Journal 51(9), 2049–2075.CrossRefGoogle Scholar
Mehrpouyan, H., Haley, B., Dong, A., Tumer, I.Y., & Hoyle, C. (2015). Resiliency analysis for complex engineered system design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(1), 93–108.Google Scholar
Monga, A., & Zuo, M.J. (1998). Optimal system design considering maintenance and warranty. Computer and Operations Research 25(9), 691–705.Google Scholar
Moullec, M.-L., Bouissou, M., Jankovic, M., Bocquet, J.C., Requillard, F., Maas, O., & Forgeot, O. (2013). Toward system architecture generation and performances assessment under uncertainty using Bayesian networks. Journal of Mechanical Design 135(4), 041002.Google Scholar
Mutha, C., Jensen, D., Tumer, I.Y., & Smidts, C. (2013). An integrated multidomain functional failure and propagation analysis approach for safe system design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27(4), 317–347.Google Scholar
Peng, T., He, J., Liu, Y., Saxena, A., Celaya, J.R., & Goebel, K. (2012). Integrated fatigue damage diagnosis and prognosis under uncertainties. Proc. Annual Conf. Prognostics and Health Management Society. Moffett Field, CA: Ames Research Center.Google Scholar
Rabas, T.J., Panchal, C.B., Sasscer, D.S., & Schaefer, R. (1993). Comparison of river-water fouling rates for spirally indented and plain tubes. Heat Transfer Engineering 14(4), 58–73.Google Scholar
Sakhrani, V. (2012). Project architecture and life-cycle performance in large infrastructure projects. Proc. 3rd Int. Conf. Complex Systems Design & Management 2012. Berlin: Springer.Google Scholar
Santander, C.F., & Sanchez-Silva, M. (2008). Design and maintenance programme optimization for large infrastructure systems. Structure and Infrastructure Engineering 4(4), 297–309.Google Scholar
Sheikh, A.K., Zubair, S.M., Younas, M., & Budair, M.O. (2001). Statistical aspects of fouling processes. Journal of Process Mechanical Engineering 215(4), 331–354.Google Scholar
Stapelberg, R.F. (2009). Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design. New York: Springer Science+Business Media.Google Scholar
Taborek, J., Aoki, T., Ritter, R.B., Palen, J.W., & Knudsen, J.G. (1972 a). Fouling: the major unresolved problem in heat transfer. Chemical Engineering Progress 68(2), 59–67.Google Scholar
Taborek, J., Aoki, T., Ritter, R.B., Palen, J.W., & Knudsen, J.G. (1972 b). Predictive methods for fouling behavior. Chemical Engineering Progress 68(7), 69–78.Google Scholar
Wang, G.G., & Shan, S. (2007). Review of metamodeling techniques in support of engineering design optimization. Journal of Mechanical Design 129(4), 370–380.Google Scholar
Wang, P., & Vachtsevanos, G. (2001). Fault prognostics using dynamic wavelet neural networks. Artificial Intelligence for Engineering, Design Analysis and Manufacturing 15(4), 349–365.Google Scholar
Wang, Z., & Wang, P. (2015). An integrated performance measure approach for system reliability analysis. Journal of Mechanical Design 137(2), 021406.Google Scholar
Wang, Z., Huang, H.Z., & Du, X. (2010). Optimal design accounting for reliability, maintenance, and warranty. Journal of Mechanical Design 132(1), 011007.Google Scholar
Xi, Z., Jing, R., Wang, P., & Hu, C. (2013). A copula-based sampling method for data-driven prognostics and health management. Proc. ASME Int. Conf. Design Theory and Methodology, Gaithersberg, MD, June 24–27.Google Scholar
Xing, Y., Ma, E.W.M., Tsui, K.L., & Pecht, M. (2011). Battery management systems in electric and hybrid vehicles. Energies 4(11), 1840–1857.Google Scholar
Xiong, F., Yin, X., Chen, W., & Yang, S. (2010). Enhanced probabilistic analytical target cascading with application to multi-scale design. Engineering Optimization 42(6), 581–592.Google Scholar
Youn, B.D., Hu, C., & Wang, P. (2011). Resilience-driven system design of complex engineered systems. Journal of Mechanical Design 133(10), 101011.Google Scholar
Youn, B.D., & Wang, P. (2009). Complementary intersection method for system reliability analysis. Journal of Mechanical Design 131(4), 041004.Google Scholar
Yu, B.Y., Honda, T., Zak, G.M., Mitsos, A., Lienhard, J., Mistry, K., Zubair, S., Sharqawy, M.H., Antar, M., & Yang, M.C. (2012). Prognosis of component degradation under uncertainty: a method for early stage design of a complex engineering system. Proc. ASME 2012 11th Biennial Conf. Engineering Systems Design and Analysis, pp. 683–694. American Society of Mechanical Engineers, Nantesin, France, July 2–4.Google Scholar
Zubair, S.M., Sheikh, A.K., Budair, M.O., & Badar, M.A. (1997). A maintenance strategy for heat transfer equipment subject to fouling: a probabilistic approach. Journal of Heat Transfer—Transactions of the ASME 119(3), 575–580.Google Scholar