Reliability analysis for multi-component systems with degradation interaction and categorized shocks

Shen, Jingyuan; Elwany, Alaa; Cui, Lirong

doi:10.1016/j.apm.2017.12.001

Cited by 62 publications

(23 citation statements)

References 36 publications

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“…To solve this type of reliability optimization problem, diverse methods can be employed. Such as the finite Markov chain imbedding approach, [36][37][38][39][40] recurrence method, 41 sequential multistate decision diagram-based approach, 42 simulation method, 43,44 heuristic algorithm, 35 mixed-integral genetic algorithm, 45 etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint optimization of reliability design and spares inventory for a use-oriented product-service system based on the electric vehicle charging station

Zhang

Zhao

Song

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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With the shift of business pattern from the traditional manufacturer orientation to customer orientation, the product-service system has become more and more popular. This paper takes an electrical vehicle charging station as the research object and builds a joint optimization model of reliability design and spares inventory with the objective of maximizing net profit under the constraints of availability and customer’s loss rate. Particularly, this model considers not only two types of services, namely fast charging and slow charging but also the transfer of customers between different types of service. This new model is more accordant with the practical circumstances by involving a series of elements of the product-service system simultaneously, such as the customer’s arrival, service, replacement maintenance and spares replenishment process. Then, the probabilistic indices related to reliability are derived by applying the continuous time Markov Process. A balance between the number of charging piles and spares inventory policy can be achieved by solving the joint optimization model. The influence of model parameters on these indices and the superiority of the proposed joint optimization model are analyzed in detail by numerical examples. Finally, some critical conclusions are obtained with the intent of promoting the system reliability and profit. Furthermore, this joint optimization model can be extended to more circumstances and provides guidances to solve the reliability optimization problems regarding a similar system.

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Section: Literature Reviewmentioning

confidence: 99%

Joint optimization of reliability design and spares inventory for a use-oriented product-service system based on the electric vehicle charging station

Zhang

Zhao

Song

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Other models considering a changing degradation rate are available in the literature such as Bian and Gebraeel. [6], Rasmekomen and Parlikad [49], Hao et al [14,18,51]. Yet, for all of the models, the idea that the amplitude of the shocks, internal and external, can vary or be random, needs to be studied further.…”

Section: State-based Interactionmentioning

confidence: 99%

Failure interaction models for multicomponent systems: a comparative study

Meango

Ouali

2018

SN Appl. Sci.

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Classical reliability models consider the failure modes of a multicomponent system as independent phenomena, even if these modes concern only one component or neighbouring components. In fact, when a failure mode occurs on a given system, causes may be related to external events or to the physical degradation of any component in the system. In both cases, failure modes can partially or totally share some root causes, which may compromise the principle of independence of failure modes. This paper reviews the failure interaction models that characterise the effect of any failure mode of a component on the failure modes of its neighbors and on the entire system. Three relevant groups of interaction models are reviewed: reliability indexes' interaction, state-based interaction and copula-based interaction. All these models share the hypothesis of stochastic dependence between failure modes, also referred to as failure interaction. They differ in their fundamental modeling concepts. Advantages and limits of each of them are emphasized in a comparative study dealing with dependency concepts, modeling methods and application domains.

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“…15 On one side, the influence of random shocks on internal degradation is that random shocks bring a jump in the degradation level of a system or accelerate the degradation rate. 16 Whereas on the other side, the degradation evolution affects the arrival intensity of random shocks, making the system more prone to hard failures when the degradation amount is higher. 17 Such a synergetic interaction leading to system failures is referred to as a dependent and competing failure process (DCFP).…”

Section: Introductionmentioning

confidence: 99%

“…These two competing failure processes are presumed to be independent in most literature while the dependency may also exist 15 . On one side, the influence of random shocks on internal degradation is that random shocks bring a jump in the degradation level of a system or accelerate the degradation rate 16 . Whereas on the other side, the degradation evolution affects the arrival intensity of random shocks, making the system more prone to hard failures when the degradation amount is higher 17 .…”

Section: Introductionmentioning

confidence: 99%

Time‐based replacement policies for a fault tolerant system subject to degradation and two types of shocks

Dong

Cao

Bae

2020

Quality & Reliability Eng

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A single component nonrepairable system suffering from both an internal stochastic degradation process and external random shocks is investigated in this paper. More specifically, the Wiener process with a positive drift coefficient is introduced to describe the gradual deterioration and the arrival number of external shocks is counted with a nonhomogeneous Poisson process (NHPP). Meanwhile, fault tolerant design is incorporated into the stochastically deterioration system so as to protect it from shock failures to some extent and is consummately addressed via a generalized m − δ shock model. From the actual engineering point of view, external shocks are typically classified into two distinct categories in this current research, that is, a minor shock (Type I shock) increasing the damage load on current degradation level and a traumatic shock (Type II shock) resulting in system catastrophic failure immediately. The closed-form expression of system survival function is derived analytically and is viewed as the generalization of existing reliability function for systems subject to dependent and competing failure processes. Based on which, two time-based maintenance (TBM) policies including an age replacement model and a block replacement model are scheduled, where the expected long-run cost rate (ELRCR) in each model is, respectively, optimized to seek the optimal replacement interval. In the illustrative example part, a subsea blowout preventer (BOP) control system is arranged to validate the theoretical results numerically. To compare which policy is more profitable under different conditions, the relative gain on optimal maintenance cost rate of the two TBM policies is presented.

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Reliability analysis for multi-component systems with degradation interaction and categorized shocks

Cited by 62 publications

References 36 publications

Joint optimization of reliability design and spares inventory for a use-oriented product-service system based on the electric vehicle charging station

Joint optimization of reliability design and spares inventory for a use-oriented product-service system based on the electric vehicle charging station

Failure interaction models for multicomponent systems: a comparative study

Time‐based replacement policies for a fault tolerant system subject to degradation and two types of shocks

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