Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Design for reliability of automotive systems; case study of dry friction clutch

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Design and production of highly reliable and safer automotive systems with longer life has been a challenge. The pressure is outcome of high competitive market and recent safety issues of reputable car manufacturers. In this paper, an integrated methodology is proposed based on design for reliability of automotive systems and considering its reliability/safety critical sub-systems. In the proposed approach, the FMEA results are used in the process of failure mode/mechanism identification. The basic failure data, mostly obtained from generic databases, are adjusted by multiplicative corrective factors to account for the design and environment impacts on system failure characteristics. The system is modeled by reliability block diagram method, simulated by Monte Carlo technique. The results of FMEA and reliability evaluation are used for system improvement by reducing the components’ failure rates and potential change of system configuration. The components’ reliability is improved by increasing the quality of components by utilization of high quality materials and modern manufacturing techniques. Modification of system configuration, e.g., adding redundancy, is an alternative for system reliability improvement in some cases. The results show that the friction lining component is the most critical elements in terms of reliability importance. After completion of this phase, an assessment is done for system reliability by comparing the system reliability targets. As a case study, dry friction clutch is studied for assessment of the proposed method. In this study, the life test requirement is researched for each component using a reliability testing techniques. Finally, the uncertainties are computed associated with the failure data and final reliability estimations and the results were presented with a confidence interval.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

FMEA:

Failure mode and effect analysis

MC:

Mote Carlo

FMECA:

Failure modes and effects and criticality analysis

FTA:

Fault tree analysis

ARINC:

Aeronautical radio incorporated

UDC:

Urban driving cycle

RPM:

Revolutions per minute

CPM:

Cycle per minute

ETC:

Emission test cycle

RBD:

Reliability block diagram

HALT:

Highly accelerated life testing

HASS:

Highly accelerated stress screening

EDRPM:

Early design reliability prediction method

MTTF:

Mean time to failure

References

  • Abo Al-kheer A, El-Hami A, Kharmanda MG, Morazán AM (2011) Reliability-based design for soil tillage machines. J Terramech 48:57–64

    Article  Google Scholar 

  • Adolfo C, Benoît IM (2007) A structured approach for the assessment of system availability and reliability using Monte Carlo simulation. J Qual Maint Eng 13(2):125–136

    Article  Google Scholar 

  • Allella F, Chiodo E, Lauria D (2005) Optimal reliability allocation under uncertain conditions, with application to hybrid electric vehicle design. Int J Qual Reliab Manag 22(6):626–641

    Article  Google Scholar 

  • Avontuur GC (2000) Reliability analysis in mechanical engineering design. Delft University Press, Delft

    Google Scholar 

  • Avontuur GC, Van der Werff K (2001) An implementation of reliability analysis in conceptual design phase of drive trains. Reliab Eng Syst Saf J 73(2):155–165

    Article  Google Scholar 

  • Bhote KR, Bhote AK (2004) World class reliability: using multiple environment overstress tests to make it happen. American Management Association, New York

    Google Scholar 

  • Blischk WR, Murthy DNP (2000) Reliability modeling, prediction, and optimization. Wiley, New York

    Book  Google Scholar 

  • Blocksim 8 User’s Guide (2012) ReliaSoft Corporation. http://www.reliasoft.com/

  • Cho TM, Lee BC (2011) Reliability-based design optimization using convex linearization and sequential optimization and reliability assessment method. Struct Saf J 33:42–50

    Article  Google Scholar 

  • Contienetal-The Future in Motion (2013) Worldwide emission standards and related regulations. Online Report

  • Denson GCW, Crowell W, Clark A, Jaworski P (1995) Nonelectric parts reliability data, vol 2, D. o. Defense

  • Dodson B, Nolan D (1999) Reliability engineering handbook. CRC, Boca Raton

    Google Scholar 

  • ECE No. 15 (1988) Exhaust emission standard. Japan Automotive Standards Internationalization Center

  • EURO Parliament (2004) EURO emission regulations

  • Guangbin Y (2007) Life cycle reliability engineering. Wiley, New York

    Google Scholar 

  • Handbook MIL-HDBK 217F (1991) Reliability prediction of electronic equipment. Revision F

  • Heisler H (2002) Advanced vehicle technology. Butter Worth Heinemann, London

    Google Scholar 

  • Jones TL (2010) Handbook of reliability prediction procedures for mechanical equipments. Naval Surface Warfare Center, Maryland

    Google Scholar 

  • Kuo W, Prasad VR (2000) An annotated overview of systems reliability optimization. IEEE Trans Reliab R-49(2):176–187

    Article  Google Scholar 

  • Kuo W, Prasad VR, Tillman FA, Hwang CL (2001a) Optimal reliability design. Cambridge University Press, Cambridge

    Google Scholar 

  • Kuo W, Prasad VR, Tillman FA, Hwang C (2001b) Optimal reliability design: fundamentals and applications. Cambridge University Press, Cambridge, pp 1–65

    Google Scholar 

  • Leemis LM (1995) Reliability—probabilistic models and statistical methods. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  • Misra KB (2008) Handbook of Performability Engineering. Springer, London

    Book  Google Scholar 

  • Modarres M (2006) Risk analysis in engineering: techniques, tools, and trends. CRC, Boca Raton

    MATH  Google Scholar 

  • Modarres M, Kaminskiy M, Krivtsov V (2010) Reliability engineering and risk analysis: a practical guide, 2nd edn. CRC, Boca Raton

    Google Scholar 

  • O’Connor AN (2011) Probability distributions used in reliability engineering, University of Maryland, Published by the Reliability Information Analysis Center (RIAC)

  • O’Halloran BM, Hoyle C, Stone RB, Tumer IY (2012) The early design reliability prediction method. ASME

  • Participants O (2002) OREDA offshore reliability data handbook, 4th edn. DNV, PO Box

  • PAYA Clutch Corp. (2013) QC Laboratory Database. Rasht

  • Popovic P, Ivanovic G (2005) Monograph “reliability design of mechanical systems” ‘VINCA’ Institute of Nuclear Sciences, Belgrade

  • Popovic P, Ivanovic G (2007) A methodology for the design of reliable vehicles in the concept stage. J Mech Eng Strojniški Vestnik 53(3/07):173–185

  • Popovic P et al (2011) Design for reliability of a vehicle transmission system. J Autom Eng. doi:10.1177/0954407011416175

    Google Scholar 

  • Rao SS, Tjandra M (1994) Reliability-based design of automotive transmission systems. Reliab Syst Saf 46(2):159–169

    Article  Google Scholar 

  • Reliasoft ALTA 8 User’s Guide (2012) ReliaSoft Corporation. http://www.reliasoft.com/

  • Salazar D, Rocco CM, Galvan BJ (2006) Optimization of constrained multiple-objective reliability problems using evolutionary algorithms. Reliab Eng Syst Saf 91:1057–1070

    Article  Google Scholar 

  • Schenkelberg F (2012) Effective reliability program traits and management. Reliability and Maintainability Symposium, Reno

    Google Scholar 

  • Sharirli M (1985) Methodology for system analysis using fault trees, success trees and importance evaluations. PHD dissertation, University of Maryland, Department of Chemical and Nuclear Engineering, College Park

  • Soleimani M, Pourgol-Mohammad M (2014) Design for reliability of complex system with limited failure data; case study of a horizontal drilling equipment. Probab Saf Assess Manag 12:1–8

    Google Scholar 

  • Soleimani M, Pourgol-Mohammad M, Rostami A, Ghanbari A (2014) Design for reliability of complex system: case study of horizontal drilling equipment with limited failure data. J Qual Reliab Eng 2015:1–13

    Article  Google Scholar 

  • Teixiera CAR, Cavalca KL (2007) Reliability as an added-value factor in an automotive clutch system. Qual Reliab Eng Int. doi:10.1002/qre.889

  • Terninko J (1997) Step-by-step QFD—customer-driven product design. St. Lucie Press, Boca Raton

    Google Scholar 

  • Wang H, Pham H (2006) Reliability and optima maintenance. Springer, London

    MATH  Google Scholar 

  • Xiao N, Huang H-Z, Li Y, He L, Jin T (2011) Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Eng Fail Anal 18:1162–1170

    Article  Google Scholar 

  • Xie LY, Zhou JY (2005) Load-strength order statistics interference models for system reliability evaluation. Int J Perform Eng 1:23–36

    Google Scholar 

  • Yalaoui A, Chatelet E, Chu C (2005) Reliability allocation problem in a series-parallel system. Reliab Eng Syst Saf 90:55–61

    Article  Google Scholar 

Download references

Acknowledgements

Authors appreciate the technical support of PAYA Clutch Co. for this project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Morteza Soleimani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pourgol-Mohammad, M., Hejazi, A., Soleimani, M. et al. Design for reliability of automotive systems; case study of dry friction clutch. Int J Syst Assur Eng Manag 8, 572–583 (2017). https://doi.org/10.1007/s13198-017-0644-2

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0644-2

Keywords