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Evaluating the reliability of multi-body mechanisms: A method considering the uncertainties of dynamic performance

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  • Wu, Jianing
  • Yan, Shaoze
  • Zuo, Ming J.

Abstract

Mechanism reliability is defined as the ability of a certain mechanism to maintain output accuracy under specified conditions. Mechanism reliability is generally assessed by the classical direct probability method (DPM) derived from the first order second moment (FOSM) method. The DPM relies strongly on the analytical form of the dynamic solution so it is not applicable to multi-body mechanisms that have only numerical solutions. In this paper, an indirect probability model (IPM) is proposed for mechanism reliability evaluation of multi-body mechanisms. IPM combines the dynamic equation, degradation function and Kaplan–Meier estimator to evaluate mechanism reliability comprehensively. Furthermore, to reduce the amount of computation in practical applications, the IPM is simplified into the indirect probability step model (IPSM). A case study of a crank–slider mechanism with clearance is investigated. Results show that relative errors between the theoretical and experimental results of mechanism reliability are less than 5%, demonstrating the effectiveness of the proposed method.

Suggested Citation

  • Wu, Jianing & Yan, Shaoze & Zuo, Ming J., 2016. "Evaluating the reliability of multi-body mechanisms: A method considering the uncertainties of dynamic performance," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 96-106.
    • Handle: RePEc:eee:reensy:v:149:y:2016:i:c:p:96-106
    DOI: 10.1016/j.ress.2015.12.013
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    References listed on IDEAS

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    1. Ramin Moghaddass & Ming Zuo, 2014. "Multistate degradation and supervised estimation methods for a condition-monitored device," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 131-148.
    2. Moghaddass, Ramin & Zuo, Ming J., 2014. "An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 92-104.
    3. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
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    Cited by:

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    2. Zhang, Qian & Pan, Ning & Meloni, Marco & Lu, Dong & Cai, Jianguo & Feng, Jian, 2021. "Reliability analysis of radially retractable roofs with revolute joint clearances," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Zhou Changcong & Ji Mengyao & Zhao Haodong & Cao Fei, 2021. "Uncertainty analysis of motion error for mechanisms and Kriging-based solutions," Journal of Risk and Reliability, , vol. 235(5), pages 731-743, October.
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    5. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.

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