Reliability assessment for multi-state systems with state transition dependency

Liu, Yu; Liu, Qinzhen; Xie, Cheng; Wei, Fayuan

doi:10.1016/j.ress.2019.03.013

Cited by 66 publications

(24 citation statements)

References 53 publications

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“…Zhang et al [29] proposed a fractional Brownian motion (FBM) based degradation model with long-range dependence and multiple modes considering the prediction of remaining useful life, and proposed a two-step method, including change-points detection and linear segments clustering, to identify the multiple modes in the degradation process. Liu [30] and Liu et al [31] proposed a method to model and evaluate system reliability for multistate systems/components with state transition dependency, and the dependency among state transitions is characterized by copula functions. Yang et al [32] studied the influence of degradation dependence on the reliability of dual-component degradation system, and established a model to predict system reliability and its residual life.…”

Section: And Vanmentioning

confidence: 99%

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

Yang

et al. 2021

IEEE Access

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This paper proposes a model of maintenance optimization for a system consisting of two dependent components in long-term operation, where both components are subject to dependent degradation. Gamma process is used to model the degradation, and Frank Copula is applied to describe the degradation dependence between the two components. We then consider degradation dependence to optimize maintenance strategy for the system to maximize its availability. The improved Artificial Bee Colony (ABC) algorithm is used to jointly determine inspection interval, opportunistic and preventive maintenance thresholds of the system. Finally, an example is provided to validate the proposed model.

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Section: And Vanmentioning

confidence: 99%

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

Yang

et al. 2021

IEEE Access

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“…Study about the hybrid approaches especially hybrid Bayesian application as an advanced method to solve reliability problems is rare, and according to big data and a lot of evidence, Bayesian infer can be investigated more especially in reliability prediction, diagnosis and fault detection, and risk reduction of the system. A recent trend in the multi-state system has been accounting for dependence modeling among components [36][37].…”

Section: Challenges and Opportunity For Reliability Engineeringmentioning

confidence: 99%

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Farsi

Zio

2019

Int J Reliabl Risk Safe Theory App

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According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.

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“…Many methods are presented to update the status with collected information, including Beyasian updating rule, Dempster combination rule, negation, dynamic model such as DEMATEL, neural model, and so on . The likelihood function is first defined as the product of probabilities.…”

Section: Preliminariesmentioning

confidence: 99%

A new soft likelihood function based on power ordered weighted average operator

Song

Deng

2019

Int J Intell Syst

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The likelihood function is widely used in data processing but the classical likelihood function is too strict to deal with data with extreme values in real applications.To address this issue, a soft likelihood function based on power ordered weighted average (POWA) operator for probability distribution is proposed. Compared with the existing soft likelihood functions, one of the main features of the proposed likelihood function is that the weighted mean is displayed by the geometric mean of the weighted probability products. As a result, the effect on final result caused by extreme data can be efficiently decreased. Numerical examples are used to illustrate the efficiency of the proposed soft likelihood function. K E Y W O R D S geometric mean, likelihood function, OWA operator, POWA operator, soft likelihood function | INTRODUCTIONDealing with uncertainty information plays an important role in real applications. 1-3 Many math tools, such as fuzzy sets, 4-6 rough sets, 7 Z-numbers, 8-11 belief structures, 12-14 D numbers 15-17 entropy function, 18,19 and evidence theory, 20-22 are presented to handle different types of uncertainty information. Among these tools, probability is heavily studied and is widely used in many applications, including decision making, 23-26 estimation, 27,28 and uncertain measurement. [29][30][31][32] One of the most used models in probability is the likelihood function, which is used when data are available to describe plausibility of a parameter value. [33][34][35] However, it is very strict in real applications since the original likelihood function made up with the probability product is too strict to show the compatibility probabilities for the different pieces of evidence since any piece of probabilities, which are low-value would greatly reduce the likelihood function.How to cite this article: Song Y, Deng Y. A new soft likelihood function based on power ordered weighted average operator. Int J Intell

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Reliability assessment for multi-state systems with state transition dependency

Cited by 66 publications

References 53 publications

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

A new soft likelihood function based on power ordered weighted average operator

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