A-IFM reliability allocation model based on multicriteria approach

Bona, Gianpaolo Di; Forcina, Antonio; Petrillo, Antonella; Felice, Fabio De; Silvestri, Alessandro

doi:10.1108/ijqrm-05-2015-0082

Cited by 30 publications

(29 citation statements)

References 19 publications

(16 reference statements)

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“…The AHP‐IFM method considers the importance of each factor and each subsystem and assigns different weights according to greater or lesser importance . For a general AHP containing a set of elements A 1 , A 2 , ⋯, A m , while a ij represents a quantified judgment on a pair ( A i , A j ), an [ m × m ] matrix A is generated as follows:

bold-italicA = ([], \begin{matrix} a_{1} / a_{1} & a_{1} / a_{2} \dots & a_{1} / a_{m} \\ \begin{matrix} a_{2} / a_{1} \\ ⋮ \end{matrix} & \begin{matrix} \begin{matrix} a_{2} / a_{2} & \dots \end{matrix} \\ \begin{matrix} ⋮ & ⋱ \end{matrix} \end{matrix} & \begin{matrix} a_{2} / a_{m} \\ ⋮ \end{matrix} \\ a_{m} / a_{1} & \begin{matrix} a_{m} / a_{2} & \dots \end{matrix} & a_{m} / a_{m} \end{matrix}) .

A pairwise comparison can be applied to subsystems, and a ranking vector z = [ z 1 z 2 ⋯ z n ] T for subsystems is obtained.…”

Section: Analysis Of Allocation Methodsmentioning

confidence: 99%

“…The AHP-IFM method considers the importance of each factor and each subsystem and assigns different weights according to greater or lesser importance. 22 For a general AHP containing a set of elements A 1 , A 2 , ⋯, A m , while a ij represents a quantified judgment on a pair (A i , A j ), an [m × m] matrix A is generated as follows:…”

Section: Analysis Of Allocation Methodsmentioning

confidence: 99%

“…According to the results of FMEA, the failure severity of failure mode S ij and the occurrence of failure mode O ij are shown in columns five and six of Table 3. The failure severity e S i and occurrence e O i of the components are calculated using formula (22) and (24), respectively. The scaling of environmental condition E i and technology level M i is based on expert experience.…”

Section: Reliability Allocationmentioning

confidence: 99%

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A reliability allocation method of mechanism considering system performance reliability

Zhang

Song

2019

Quality & Reliability Eng

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It is generally believed that the reliability of a mechanical system is determined by its composition. The system operates properly when all of its components do not fail. Based on this assumption, the reliability of the system can be represented by the reliability of its components. A problem arises when applying this hypothesis to a system containing motion mechanisms. There is a phenomenon in motion mechanism that the components do not happen structural failure (we call it “Type I failure”) and joint failure (we call it “Type II failure”), but the function of the mechanism cannot meet the requirements (we call it “Type III failure”). A reliability allocation method, which synthetically considers the composition and Type III failure modes of the motion mechanism, is proposed to solve this problem. A relative dispersion factor is introduced to describe the failure dependence of components and is proposed to calculate the complexity and criticality. A series system reliability allocation model considering three types of failure modes is established. Finally, using an airplane gear door lock mechanism as an example, a comparative analysis of the system reliability allocation results with and without considering Type III failure modes is made. The allocation results show the component reliability value without considering Type III failure modes is less than that when considering them, which will increase the system hazards. The result considering Type III failure modes is more reasonable than that from the traditional method.

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bold-italicA = ([], \begin{matrix} a_{1} / a_{1} & a_{1} / a_{2} \dots & a_{1} / a_{m} \\ \begin{matrix} a_{2} / a_{1} \\ ⋮ \end{matrix} & \begin{matrix} \begin{matrix} a_{2} / a_{2} & \dots \end{matrix} \\ \begin{matrix} ⋮ & ⋱ \end{matrix} \end{matrix} & \begin{matrix} a_{2} / a_{m} \\ ⋮ \end{matrix} \\ a_{m} / a_{1} & \begin{matrix} a_{m} / a_{2} & \dots \end{matrix} & a_{m} / a_{m} \end{matrix}) .

A pairwise comparison can be applied to subsystems, and a ranking vector z = [ z 1 z 2 ⋯ z n ] T for subsystems is obtained.…”

Section: Analysis Of Allocation Methodsmentioning

confidence: 99%

Section: Analysis Of Allocation Methodsmentioning

confidence: 99%

Section: Reliability Allocationmentioning

confidence: 99%

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A reliability allocation method of mechanism considering system performance reliability

Zhang

Song

2019

Quality & Reliability Eng

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“…This categorization has been performed on the basis of the types of their decision variables . Analytic integrated factors method based on multicriteria approach has been employed by Di Bona et al for reliability allocation. Yadav and Zhuang proposed a modified critical measure for allocating system level reliability improvement goal to subsystems.…”

Section: Literature Reviewmentioning

confidence: 99%

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

Kumar

Pant

Ram

2016

Quality & Reliability Eng

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For the past two decades, nature-inspired optimization algorithms have gained enormous popularity among the researchers. On the other hand, complex system reliability optimization problems, which are nonlinear programming problems in nature, are proved to be non-deterministic polynomial-time hard (NP-hard) from a computational point of view. In this work, few complex reliability optimization problems are solved by using a very recent nature-inspired metaheuristic called gray wolf optimizer (GWO) algorithm. GWO mimics the chasing, hunting, and the hierarchal behavior of gray wolves. The results obtained by GWO are compared with those of some recent and popular metaheuristic such as the cuckoo search algorithm, particle swarm optimization, ant colony optimization, and simulated annealing. This comparative study shows that the results obtained by GWO are either superior or competitive to the results that have been obtained by these well-known metaheuristic mentioned earlier.SA =simulated annealing; GA = genetic algorithm; TS = tabu search; PSO = particle swarm optimization; CSA = cuckoo search algorithm; DP = dynamic programming.A. KUMAR, S. PANT AND M. RAM

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“…,Cheng et al (2014),Chatterjee et al (2015),Subhashis et al (2015),Chen et al (2016),Di Bona et al (2016),Li et al (2016),Ma et al (2016) and can be given as example studies of using AHP in reliability allocation process. Decision making trial and evaluation laboratory (DEMATEL) method besides the AHP is another preferred MCDM method in the literature.…”

mentioning

confidence: 99%

A knowledge based hierarchical reliability allocation (HIRAL) approach for shipboard systems

Çiçek¹

2020

Preprint

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Reliability has become a greater concern in ship-board systems due to increasing technology, system complexity and multiple design demands. Enhancement the reliability of ship-board systems promotes to ensure safe and continuous operation onboard a ship. To enhance the reliability of ship-board system, it is essential to figure out each individual component reliability. Within this scope, reliability allocation analysis turns into an onerous task to enhance the reliability of ship-board systems through identification of possible component-based design, construction and operation optimizations. This study proposes a hybrid reliability allocation methodology based on hierarchical structure with the integration of analytic hierarchy process (AHP), data envelopment analysis (DEA) and feasibility of objectives (FOO) methods. The proposed methodology provides reliability allocation analysis for systems with any number of components. Also, the study figures out the usefulness of the adaptation of AHP-DEA into reliability allocation analysis. To demonstrate the applicability of proposed methodology, a case study on steering gear system is presented.

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A-IFM reliability allocation model based on multicriteria approach

Cited by 30 publications

References 19 publications

A reliability allocation method of mechanism considering system performance reliability

A reliability allocation method of mechanism considering system performance reliability

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

A knowledge based hierarchical reliability allocation (HIRAL) approach for shipboard systems

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