Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T07:52:32.310Z Has data issue: false hasContentIssue false
  • English
  • Français

Model-based reliability analysis

Published online by Cambridge University Press:  14 July 2016

Julia Lindén ,
Ulf Sellgren  and
Anders Söderberg
Julia Lindén*
Affiliation:
Scania CV AB, Södertälje, Sweden Machine Design Department, KTH Royal Institute of Technology, Stockholm, Sweden
Ulf Sellgren
Affiliation:
Machine Design Department, KTH Royal Institute of Technology, Stockholm, Sweden
Anders Söderberg
Affiliation:
Machine Design Department, KTH Royal Institute of Technology, Stockholm, Sweden
*
Reprint requests to: Julia Lindén, Brinellv, Machine Design Department, KTH Royal Institute of Technology, Brinellvägen 83, Stockholm 10044, Sweden. E-mail: Julia.linden@scania.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The main function of a heavy truck is to transport goods, with ton-kilometers/year as an example of a major quantitative performance measure. Furthermore, the truck is directly operated by a driver, who has several additional functional requirements, of both ergonomic and communicative characters. Failure of these functions may be a subjective experience, differing between drivers, but the failures are still important. Today's just-in-time delivery systems rely on getting the goods on time, and this requires high availability. Availability is reduced not only by technical failures but also by subjectively experienced failures, because these also require repairs, or downtime. Product reliability is a systems property that cannot be attributed to a single component. It is in many cases related to interaction between components, or to interaction between humans and the technical system, in the case of subjectively experienced failures. Reliability assessments of systems with interactive functions require a system model that includes the interfaces between the technical system and human features that are carriers of interactive functions. This paper proposes a model of system architecture, for the purpose of reliability assessments, that integrates different and complementary representations, such as function–means diagrams and a design structure matrix. The novelty of the presented approach is that it treats and integrates the technical and the human subsystems through the human–technical system interfaces. The proposed systems reliability approach is described and verified with a component analysis case study of an extended truck cab and driver system.

Keywords

Extended Design Structure MatrixInteractive functionReliability Estimation
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Special Issue 3: System Architecture Design , August 2016 , pp. 277 - 288
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

Al-Mashari, M., Zairi, M., & Ginn, D. (2005). Key enablers for the effective implementation of QFD: a critical analysis. Industrial Management & Data Systems 105(9), 1245–1260. doi:10.1108/02635570510633284CrossRefGoogle Scholar
Andreasen, M.M. (1980). Machine design methods based on systematic approach—contribution to design theory. PhD Thesis. Lund University, Sweden.Google Scholar
Belsus, S.M., Sankar, G., & Sharma, A. (2010). Vehicle Reliability Estimation Model for Concept Vehicle Target Setting and Identification of Critical Parameters Influencing System Reliability, Technical Report. SAE International. doi:10.4271/2010-32-0068Google Scholar
Bly, S., Schilit, B., McDonald, D.W., Rosario, B., & Saint-Hilaire, Y. (2006). Broken expectations in the digital home. Proc. CHI ‘06 Extended Abstracts on Human Factors in Computing Systems—CHI ‘06, p. 568, Montreal, April 24–27. doi:10.1145/1125451.1125571Google Scholar
Borjesson, F., & Hölttä-Otto, K. (2013). A module generation algorithm for product architecture based on component interactions and strategic drivers. Research in Engineering Design 25(1), 31–51. doi:10.1007/s00163-013-0164-2Google Scholar
Braglia, M., Fantoni, G., & Frosolinir, M. (2007). The house of reliability. International Journal of Quality & Reliability Management 24(4), 420–440.Google Scholar
Brombacher, A.C., Sander, P.C., Sonnemans, P.J.M., & Rouvroye, J.L. (2005). Managing product reliability in business processes “under pressure.” Reliability Engineering and System Safety 88(2), 137–146. doi:10.1016/j.ress.2004.07.003Google Scholar
Collins, J.A., Hagan, B.T., & Bratt, H.M. (1976). The failure-experience matrix—a useful design tool. Journal of Manufacturing Science and Engineering, Transactions of the ASME 75, 1074–1079.Google Scholar
Davis, T.P. (2006). Science, engineering, and statistics. Applied Stochastic Models in Business and Industry 22, 401–430. doi:10.1002/asmbGoogle 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. doi:10.1017/S089006041400050XGoogle Scholar
Erixon, G. (1998). Modular function deployment: a method for product modularisation. PhD Thesis. KTH Royal Institute of Technology, Stockholm.Google Scholar
Gnamm, J., Lundgren, J., Stricker, K., & Nilvall, M. (2012). Winning in Europe: Truck Strategies in Europe for the Next Decade. Technical Report. Boston: Bain & Company, Inc.Google Scholar
Grantham Lough, K.A., Stone, R.B., & Tumer, I.Y. (2008). Failure prevention in design through effective catalogue utilization of historical failure events. Journal of Failure Analysis and Prevention 8(5), 469–481. doi:10.1007/s11668-008-9160-7Google Scholar
Hirtz, J., Stone, R.B., & McAdams, D.A. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13, 65–82. doi:10.1007/s00163-001-0008-3Google Scholar
Katz, G. (2007). Quality function deployment and the house of quality. In PDMA ToolBook 3 for New Product Development (Griffin, A., & Somermeyer, S., Eds.). Hoboken, NJ: Wiley.Google Scholar
Kim, C. (2014). User characteristics and behaviour in operating annoying electronic products. International Journal of Design 8(1), 93–108.Google Scholar
Krus, D., & Grantham Lough, K. (2009). Function-based failure propagation for conceptual design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23(4), 409. doi:10.1017/S0890060409000158CrossRefGoogle Scholar
Ku, H.H. (1966). Notes on the use of propagation of error formulas. Journal of Research of the National Bureau of Standards 70C(4), 263. doi:10.6028/jres.070C.025Google Scholar
Mutha, C., Jensen, D., Tumer, I., & 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. doi:10.1017/S0890060413000152Google Scholar
Ouden, E. den, Yuan, L., Sonnemans, P.J.M., & Brombacher, A.C. (2006). Quality and reliability problems from a consumer's perspective: an increasing problem overlooked by businesses? Quality and Reliability Engineering International 22, 821–838.Google Scholar
Qatu, M.S., King, R., Wheeler, R., & Shubailat, O. (2011). Vehicle design for robust driveline NVH due to imbalance and runout using a Monte Carlo process. SAE International Journal of Passenger Cars—Mechanical Systems 4(2), 1033–1038. doi:10.4271/2011-01-1546Google Scholar
Rahimi, M., & Rausand, M. (2013). Prediction of failure rates for new subsea systems: a practical approach and an illustrative example. Journal of Risk and Reliability 227(6), 629–640. doi:10.1177/1748006X13492954Google Scholar
Sellgren, U., & Andersson, S. (2005). The concept of functional surfaces as carriers of interactive properties. Proc. Int. Conf. Engineering Design ICED 05, Melbourne, August 15–18.Google Scholar
Steward, D.V. (1981). The design structure system: a method for managing the design of complex systems. IEEE Transactions on Engineering Management 28(3), 71–74. doi:10.1109/TEM.1981.6448589Google Scholar