Reliability analysis and prediction of mixed mode load using Markov Chain Model

N. Nikabdullah, S. S K Singh, R. Alebrahim, M. A. Azizi, K. A. Elwaleed, Mohd. Salmi Md. Noorani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading.

Original languageEnglish
Title of host publicationAIP Conference Proceedings
PublisherAmerican Institute of Physics Inc.
Pages918-924
Number of pages7
Volume1602
ISBN (Print)9780735412361
DOIs
Publication statusPublished - 2014
Event3rd International Conference on Mathematical Sciences, ICMS 2013 - Kuala Lumpur
Duration: 17 Dec 201319 Dec 2013

Other

Other3rd International Conference on Mathematical Sciences, ICMS 2013
CityKuala Lumpur
Period17/12/1319/12/13

Fingerprint

reliability analysis
Markov chains
torsional stress
predictions
torsion
stress concentration
curves
hazards
safety
distribution functions
cycles

Keywords

  • Markov Chain
  • Probability
  • Reliability
  • Weibull

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Nikabdullah, N., Singh, S. S. K., Alebrahim, R., Azizi, M. A., Elwaleed, K. A., & Md. Noorani, M. S. (2014). Reliability analysis and prediction of mixed mode load using Markov Chain Model. In AIP Conference Proceedings (Vol. 1602, pp. 918-924). American Institute of Physics Inc.. https://doi.org/10.1063/1.4882593

Reliability analysis and prediction of mixed mode load using Markov Chain Model. / Nikabdullah, N.; Singh, S. S K; Alebrahim, R.; Azizi, M. A.; Elwaleed, K. A.; Md. Noorani, Mohd. Salmi.

AIP Conference Proceedings. Vol. 1602 American Institute of Physics Inc., 2014. p. 918-924.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nikabdullah, N, Singh, SSK, Alebrahim, R, Azizi, MA, Elwaleed, KA & Md. Noorani, MS 2014, Reliability analysis and prediction of mixed mode load using Markov Chain Model. in AIP Conference Proceedings. vol. 1602, American Institute of Physics Inc., pp. 918-924, 3rd International Conference on Mathematical Sciences, ICMS 2013, Kuala Lumpur, 17/12/13. https://doi.org/10.1063/1.4882593
Nikabdullah N, Singh SSK, Alebrahim R, Azizi MA, Elwaleed KA, Md. Noorani MS. Reliability analysis and prediction of mixed mode load using Markov Chain Model. In AIP Conference Proceedings. Vol. 1602. American Institute of Physics Inc. 2014. p. 918-924 https://doi.org/10.1063/1.4882593
Nikabdullah, N. ; Singh, S. S K ; Alebrahim, R. ; Azizi, M. A. ; Elwaleed, K. A. ; Md. Noorani, Mohd. Salmi. / Reliability analysis and prediction of mixed mode load using Markov Chain Model. AIP Conference Proceedings. Vol. 1602 American Institute of Physics Inc., 2014. pp. 918-924
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