Reliability analysis of load-sharing parallel systems considering equivalent strength degradation

2020 ◽  
pp. 1748006X2096842
Author(s):  
Xinshui Yu ◽  
Tianxiang Yu ◽  
Kunling Song ◽  
Bifeng Song
Keyword(s):  
Parallel Systems ◽  
Original System ◽  
Load Sharing ◽  
Degradation Process ◽  
Strength Degradation ◽  
Parallel System ◽  
Failure Behavior ◽  
Initial Strength ◽  
Reliability Method ◽  
Equivalent Strength

In this paper, a new reliability method for load-sharing parallel systems with dependent components that share the workload equally before and after some components have failed is studied. In the working process of a load-sharing parallel system, after the failure of some components, the surviving components share the original system workload with higher components loads. The states of all the components are dependent. The failure behavior of a component impacts the strength degradation process of the remaining working components. For a load-sharing parallel system, one component works the whole system works, which means the component with the largest initial strength works, the whole system works. Firstly, we use the equivalent strength degradation theory to get the remaining strength of the component with the largest initial strength after some components fail. Then, the stress-strength interference model will be used to calculate the reliability after some components fail. Finally, the proposed method is illustrated by a numerical example and verified by the Monte Carlo simulation method.

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

2014 ◽  
Vol 229 (13) ◽  
pp. 2484-2495 ◽  
Author(s):  
Peng Gao ◽  
Liyang Xie
Keyword(s):  
Failure Rate ◽  
Mechanical Load ◽  
Parallel Systems ◽  
Load Sharing ◽  
Strength Degradation ◽  
Random Load ◽  
Dynamic Reliability ◽  
Reliability Models ◽  
Load Redistribution ◽  
Mechanical Components

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.

scholarly journals ON STOCHASTIC COMPARISONS FOR LOAD-SHARING SERIES AND PARALLEL SYSTEMS

2016 ◽  
Vol 31 (3) ◽  
pp. 311-329 ◽  
Author(s):  
Maxim Finkelstein ◽  
Nil Kamal Hazra
Keyword(s):  
Parallel Systems ◽  
Load Sharing ◽  
Parallel System ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Stochastic Comparisons ◽  
Cumulative Exposure Model ◽  
Redundant Component ◽  
Allocation Strategies ◽  
Additional Assumptions

We study the allocation strategies for redundant components in the load-sharing series/parallel systems. We show that under the specified assumptions, the allocation of a redundant component to the stochastically weakest (strongest) component of a series (parallel) system is the best strategy to achieve its maximal reliability. The results have been studied under cumulative exposure model and for a general scenario as well. They have a clear intuitive meaning; however, the corresponding additional assumptions are not obvious, which can be seen from the proofs of our theorems.

Comparison of the Cross Sectional Area, the Loss in Volume and the Mechanical Properties of LAE442 and MgCa0.8 as Resorbable Magnesium Alloy Implants after 12 Months Implantation Duration

2010 ◽  
Vol 638-642 ◽  
pp. 675-680 ◽  
Author(s):  
Martina Thomann ◽  
Nina von der Höh ◽  
Dirk Bormann ◽  
Dina Rittershaus ◽  
C. Krause ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Cross Sections ◽  
Degradation Process ◽  
Cross Sectional Area ◽  
Bending Test ◽  
Sectional Area ◽  
Cross Sectional ◽  
Initial Strength ◽  
Three Point Bending ◽  
The Cross

Current research focuses on magnesium based alloys in the course of searching a resorbable osteosynthetic material which provides sufficient mechanical properties besides a good biocompatibility. Previous studies reported on a favorable biocompatibility of the alloys LAE442 and MgCa0.8. The present study compared the degradation process of cylindrical LAE442 and MgCa0.8 implants after 12 months implantation duration. Therefore, 10 extruded implants (2.5 x 25 mm, cross sectional area 4.9 mm²) of both alloys were implanted into the medullary cavity of both tibiae of rabbits for 12 months. After euthanization, the right bone-implant-compound was scanned in a µ-computed tomograph (µCT80, ScancoMedical) and nine uniformly distributed cross-sections of each implant were used to determine the residual implants´ cross sectional area (Software AxioVisionRelease 4.5, Zeiss). Left implants were taken out of the bone carefully. After weighing, a three-point bending test was carried out. LAE442 implants degraded obviously slower and more homogeneously than MgCa0.8. The mean residual cross sectional area of LAE442 implants was 4.7 ± 0.07 mm². MgCa0.8 showed an area of only 2.18 ± 1.03 mm². In contrast, the loss in volume of LAE442 pins was more obvious. They lost 64 % of their initial weight. The volume of MgCa0.8 reduced clearly to 54.4 % which corresponds to the cross sectional area results. Three point bending tests revealed that LAE442 showed a loss in strength of 71.2 % while MgCa0.8 lost 85.6 % of its initial strength. All results indicated that LAE442 implants degraded slowly, probably due to the formation of a very obvious degradation layer. Degradation of MgCa0.8 implants was far advanced.

Stochastic comparisons of series and parallel systems with independent heterogeneous Gumbel and truncated Gumbel components

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatih Kızılaslan
Keyword(s):  
Likelihood Ratio ◽  
Hazard Rate ◽  
Design Methodology ◽  
Parallel Systems ◽  
Parallel System ◽  
Series System ◽  
Stochastic Comparisons ◽  
Content Type ◽  
Reversed Hazard Rate ◽  
The Relationship

PurposeThe purpose of this paper is to investigate the stochastic comparisons of the parallel system with independent heterogeneous Gumbel components and series and parallel systems with independent heterogeneous truncated Gumbel components in terms of various stochastic orderings.Design/methodology/approachThe obtained results in this paper are obtained by using the vector majorization methods and results. First, the components of series and parallel systems are heterogeneous and having Gumbel or truncated Gumbel distributions. Second, multiple-outlier truncated Gumbel models are discussed for these systems. Then, the relationship between the systems having Gumbel components and Weibull components are considered. Finally, Monte Carlo simulations are performed to illustrate some obtained results.FindingsThe reversed hazard rate and likelihood ratio orderings are obtained for the parallel system of Gumbel components. Using these results, similar new results are derived for the series system of Weibull components. Stochastic comparisons for the series and parallel systems having truncated Gumbel components are established in terms of hazard rate, likelihood ratio and reversed hazard rate orderings. Some new results are also derived for the series and parallel systems of upper-truncated Weibull components.Originality/valueTo the best of our knowledge thus far, stochastic comparisons of series and parallel systems with Gumbel or truncated Gumble components have not been considered in the literature. Moreover, new results for Weibull and upper-truncated Weibull components are presented based on Gumbel case results.

Hardware support for load sharing in parallel systems

1996 ◽  
Vol 42 (2) ◽  
pp. 129-143 ◽  
Author(s):  
Marco Avvenuti ◽  
Luigi Rizzo ◽  
Lorenzo Vicisano
Keyword(s):  
Parallel Systems ◽  
Load Sharing ◽  
Hardware Support

scholarly journals Stochastic Order Relations Among Parallel Systems from Weibull Distributions

2015 ◽  
Vol 52 (01) ◽  
pp. 102-116 ◽  
Author(s):  
Nuria Torrado ◽  
Subhash C. Kochar
Keyword(s):  
Likelihood Ratio ◽  
Hazard Rate ◽  
Stochastic Order ◽  
Random Variables ◽  
Parallel Systems ◽  
Parallel System ◽  
Weibull Distributions ◽  
Scale Parameters ◽  
Order Relations ◽  
Stochastic Order Relations

Let X λ1 , X λ2 , …, X λ n be independent Weibull random variables with X λ i ∼ W(α, λ i ), where λ i > 0 for i = 1, …, n. Let X n:n λ denote the lifetime of the parallel system formed from X λ1 , X λ2 , …, X λ n . We investigate the effect of the changes in the scale parameters (λ1, …, λ n ) on the magnitude of X n:n λ according to reverse hazard rate and likelihood ratio orderings.

Appraisal of a Novel Reliability Optimisation Model Using Hypothetical Problems

2014 ◽  
Vol 12 ◽  
pp. 1-14 ◽  
Author(s):  
Stephen B. Twum ◽  
Elaine Aspinwall
Keyword(s):  
Lower Bounds ◽  
System Reliability ◽  
Convex Combination ◽  
Parallel Systems ◽  
Parallel System ◽  
Weighting Scheme ◽  
Optimisation Algorithm ◽  
Level Of Difficulty ◽  
Optimisation Model ◽  
Sensitive Parameters

This paper examines further a novel series-parallel system reliability optimisation model and methodology earlier developed by the authors. The aim was to investigate how the model constructed for hypothetical series-parallel systems, reacted to slight variations to its parameters, which are the weights for the subsystem reliabilities, the feasibility factors which quantify the level of difficulty of improving a component’s reliability, and the subsystem reliability lower bounds. The optimisation algorithm based on a convex combination of the subsystem reliabilities was run using various combinations of the values of these parameters. The results show that the model was stable under the weighting scheme used and the most sensitive parameters were the feasibility factors and the subsystem reliability lower bounds.

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