Reliability analysis of a multi-state system with identical units having two dependent components

Iscioglu, Funda; Erem, Aysegul

doi:10.1177/1748006x20957476

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…However, engineering systems in real-world typically presents multiple states in addition to the states of completely working or totally failed. 1 The system has a finite number of states with corresponding different performance rates, such as electrical power system, 2,3 pipe transmission system, 4 manufacturing system, 5 is considered to be a multi-state system (MSS). Besides characterizing the degradation process of the component or the system, the MSS model can reveal the impact of component performance on the system performance and its availability.…”

Section: Introductionmentioning

confidence: 99%

Redundancy allocation optimization for multi-state system with hierarchical performance requirements

Zhou

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Conventional redundancy allocation optimization model is usually measured by single performance. It meets difficulties in modeling the redundancy allocation optimization of multi-state system (MSS) with hierarchical performance requirements. This study proposes a generalized optimization model for MSS. This model concentrates on the redundancy allocation problem of the MSS with the inter-level dependent performances requirements. In the case of the minimum cost or maximum availability of the system, the aim of this model is to optimize system configuration, such as the economic numbers and versions for the multilevel heterogeneous components with known reliability and cost characteristics. Firstly, two algorithms to evaluate the system availability are introduced. A modified universal generation function (UGF) algorithm combining hierarchical operators is developed to evaluate the accurate availability for the system. Then the recursive algorithm (RA) is also used to obtain the lower and upper bounds of system availability. Secondly, compared with the traditional optimization model for the single level system, the proposed model for the hierarchical system has more decision variables which lead to difficult computation. Therefore, the genetic algorithm (GA) is applied to solve the redundancy allocation, especially the optimal numbers and versions of the different-level components simultaneously. Finally, a realistic power system verifies the correctness and validity of the suggested model. In conclusion, the results show that the above model tends to be more flexible and effective in the redundancy allocation optimization. Furthermore, this model helps the engineer in the reliability design optimization for the complex systems.

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Section: Introductionmentioning

confidence: 99%

Redundancy allocation optimization for multi-state system with hierarchical performance requirements

Zhou

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…5,6 Some of the studies are related to the application of shock models in multi-state systems. 7,8 By considering c1 and c2 to be two critical levels, for example, Eryilmaz 7 investigated a multi-state system. A shock with a magnitude between c1 and c2 causes a partial failure.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of an extended shock model

Poursaeed

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Suppose that a system is subject to a sequence of shocks which occur with probability p in any period of time [Formula: see text], and suppose that [Formula: see text] and [Formula: see text] are two critical levels ([Formula: see text]). The system fails when the time interval between two consecutive shocks is less than [Formula: see text], and the time interval bigger than [Formula: see text] has no effect on the system activity. In addition, the system fails with a probability of, say, [Formula: see text], when the time interval varies between [Formula: see text] and [Formula: see text]. Therefore, this model can be regarded as an extension of discrete time version of [Formula: see text]-shock model, and such an idea can be also applied in the extension of other shock models. The present study obtains the reliability function and the probability generating function of the system’s lifetime under this model. The present study offers some properties of the system and refers to a generalization of the new model. In addition, the mean time of the system’s failure is obtained under reduced efficiency which is created when the time between two consecutive shocks varies between [Formula: see text] and [Formula: see text] for the first time.

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“…Liu et al [18] modeled the stochastic dependency among state transitions of a MSS or component via copula functions and studied the reliability assessment of MSS with state transition dependency. Iscioglu and Erem [19] evaluated the reliability of a MSS consisting of n identical independent units with two different types of dependency among components. Mi et al [20] developed an evidential network-based method to analyze the reliability of complex MSS with epistemic uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A generic physics-informed neural network-based framework for reliability assessment of multi-state systems

Zhou¹,

Droguett²,

Mosleh³

2021

Preprint

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In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems (MSSs). The proposed methodology consists of two major steps. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups are constructed to encode the initial condition and state transitions governed by ordinary differential equations (ODEs) in MSS. Next, we tackle the problem of high imbalance in the magnitude of the back-propagated gradients in PINN from a multi-task learning perspective. Particularly, we treat each element in the loss function as an individual task, and adopt a gradient surgery approach named projecting conflicting gradients (PCGrad), where a task's gradient is projected onto the norm plane of any other task that has a conflicting gradient. The gradient projection operation significantly mitigates the detrimental effects caused by the gradient interference when training PINN, thus accelerating the convergence speed of PINN to high-precision solutions to MSS reliability assessment. With the proposed PINN-based framework, we investigate its applications for MSS reliability assessment in several different contexts in terms of time-independent or dependent state transitions and system scales varying from small to medium. The results demonstrate that the proposed PINN-based framework shows generic and remarkable performance in MSS reliability assessment, and the incorporation of PCGrad in PINN leads to substantial improvement in solution quality and convergence speed.

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Reliability analysis of a multi-state system with identical units having two dependent components

Cited by 4 publications

References 34 publications

Redundancy allocation optimization for multi-state system with hierarchical performance requirements

Redundancy allocation optimization for multi-state system with hierarchical performance requirements

Reliability analysis of an extended shock model

A generic physics-informed neural network-based framework for reliability assessment of multi-state systems

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