Dynamic reliability models of mechanical load-sharing parallel systems considering strength degradation of components

Abstract

Conventional reliability analysis of load-sharing parallel systems is mainly based on failure rate of components, in which failure dependence of components and load redistribution are also characterized by specified failure rates. However, the failure rate of mechanical components always varies with time, which is difficult to measure. Therefore, in this paper, quantitative dynamic reliability models of mechanical load-sharing parallel systems are developed in terms of stress parameters and strength parameters rather than failure rate of components, which consider the degradation mechanism of mechanical components. The proposed models take into account the strength degradation path dependence (SDPD) of a component, the strength degradation process dependence between different components in a system, and the random load redistribution. In addition, Monte Carlo simulation is carried out to verify the proposed models. The results show that SDPD and the load-sharing effect have considerable influences on dynamic reliability of mechanical load-sharing parallel systems.

Keywords

Parallel system dynamic reliability strength degradation load-sharing load redistribution

Get full access to this article

View all access options for this article.

References

Moss

Andrews

. Reliability assessment of mechanical systems. Proc IMechE, Part F: J Rail and Rapid Transit 1996; 210: 205–216.

Hao

. Conceptual designs of multi-degree of freedom compliant parallel manipulators composed of wire-beam based compliant mechanisms. Proc IMechE, Part C: J Mechanical Engineering Science 2015. 229: 538–555.

Yan

. An approach to system reliability prediction for mechanical equipment using fuzzy reasoning Petri net. Proc IMechE, Part O: Journal of Risk and Reliability 2014; 228: 39–51.

Kececioglu

. Reliability analysis of mechanical components and systems. Nucl Eng Des 1972; 19: 259–290.

You

Lee

. A critical review on multiaxial fatigue assessments of metals. Int J Fatigue 1996; 18: 235–244.

Liu

Frangopol

Kwon

. Fatigue reliability assessment of retrofitted steel bridges integrating monitored data. Struc Saf 2010; 32: 77–89.

Valor

Caleyo

Hallen

. Reliability assessment of buried pipelines based on different corrosion rate models. Corros Sci 2013; 66: 78–87.

Ding

. Cutting tool wear monitoring for reliability analysis using proportional hazards model. Int J Adv Manuf Tech 2011; 57: 565–574.

Gao

Yan

Xie

. Dynamic reliability analysis of mechanical components based on equivalent strength degradation paths. Stroj Vestn J Mech Eng 2013; 59: 387–399.

10.

Gupta

Mittal

Batra

. Reliability parameters of a power generating system with shared Load. J Math Stat 2007; 3: 211–211.

11.

Shao

Lamberson

. Modeling a shared-load k-out-of-n: G system. IEEE T Reliab 1991; 40: 205–209.

12.

Scheuer

. Reliability of an m-out of-n system when component failure induces higher failure rates in survivors. IEEE T Reliab 1988; 37: 73–74.

13.

Park

. Parameter estimation for the reliability of load-sharing systems. IIE Trans 2010; 42: 753–765.

14.

Liu

. Reliability of a load-sharing k-out-of-n: G system: non-iid components with arbitrary distributions. IEEE T Reliab 1998; 47: 279–284.

15.

Mohammad R, Kalam A and Amari SV. Reliability evaluation of phased-mission systems with load-sharing components. Reliability and Maintainability Symposium (RAMS), Reno, Nevada, 23–26 Jan 2012. New York: IEEE, pp.1–6.

16.

Qin

Xie

. Modern handbook of mechanical design, 1st ed. Beijing: Chemical Industry Press, 2013, pp. 143–144.

17.

. Structural reliability analysis under dead load and fatigue load. Acta Armamentar 2007; 28: 1473–1477.

18.

Park

Padgett

. Analysis of strength distributions of multi-modal failures using the EM algorithm. J Stat Comput Sim 2006; 76: 619–636.

19.

Park

. Parameter estimation from load-sharing system data using the expectation-maximization algorithm. IIE Trans 2013; 45: 147–163.

20.

Park

. Parameter estimation of incomplete data in competing risks using the EM algorithm. IEEE T Reliab 2005; 54: 282–290.