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Probabilistic Fatigue Life Prediction for Crankshaft of Marine Diesel Engine

  • Xiaopeng Chang
  • , Xiaohui Feng
  • , Jingjing He*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Diesel engines are the most commonly used power plant of ships in the world. The crankshaft which transforms the translational motion generated by combustion to rotational motion, is the critical components which affects the fatigue life of diesel engine. In this paper, a probabilistic fatigue life prediction method for crankshaft is proposed. A three-dimensional finite element analysis (FEA) is used to analyze the distribution of equivalent von Mises stress of the crankshaft. The S-N/ε-N curve of the material obtained from existing literature is used to evaluate the parameters of Coffin-Manson model. The life model parameters are statistically identified using Bayesian estimation with Markov chain simulations. In addition to simulation examples, an actual case from Shanghai Marine Diesel Engine Research Institute is carried out using this probabilistic fatigue life prediction method. Then the probabilistic life and material dispersion are obtained, which is helpful for the design, replacement and maintenance strategy of the crankshaft.

Original languageEnglish
Title of host publicationIET Conference Proceedings
PublisherInstitution of Engineering and Technology
Pages1725-1730
Number of pages6
Volume2022
Edition21
ISBN (Electronic)9781839538360
DOIs
StatePublished - 2022
Event12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022 - Emeishan, China
Duration: 27 Jul 202230 Jul 2022

Conference

Conference12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022
Country/TerritoryChina
CityEmeishan
Period27/07/2230/07/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • crankshaft
  • durability simulation
  • finite element analysis
  • probability life prediction

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