Reliability and Mean Residual Life of Complex Systems With Two Dependent Components Per Element

Bayramoğlu, İsmihan

doi:10.1109/tr.2013.2241135

Cited by 20 publications

(11 citation statements)

References 39 publications

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“…However, the RUL is difficult to estimate in a complex aircraft engine system. The RUL of single component may be long, but in this complex system there are many components which are related to each other, and their interactions can have harmful effects on the aircraft engine system RUL [26]. To improve the accuracy of the RUL prognostics, the proposed PHM-oriented fusion prognostic framework fuses the experience-based and the data-driven prognostic approaches.…”

Section: Phm For Aircraft Enginesmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

131

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As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.Index Terms-Fusion prognostics, sensor data, prognostics and health management, remaining useful life, aircraft engines.

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Section: Phm For Aircraft Enginesmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

131

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“…b. Given the initial values of the parameters c 0 ,c 1 ,c 2 ,P and the stress level S i , compute the values of the parameters λ il = c l S P i , l = 0, 1, 2, then generate three independent ordered samples t il1 ð Þ ,t il2 ð Þ , …,t iln i Suppose k = 3, the accelerated stresses levels are (S 1 , S 2 , S 3 ) = (3,4,6) and the normal stress level is S 0 = 2. The parameters (c 0 , c 1 , c 2 , P) = (0.05,0.045,0.035,2) in the accelerated model, then the values of (λ 10 , λ 11 , λ 12 ) = (0.4500,0.4050,0.3150), (λ 20 , λ 21 , λ 22 ) = (0.8000,0.7200,0.5600) and (λ 30 , λ 31 , λ 32 ) = (1.8000,1.6200,1.2600).…”

Section: Simulation Studymentioning

confidence: 99%

“…In reliability and survival analysis, an item can be failed due to several reasons, but only the minimum failure time and the related failure cause index can be observed. This type of failure mechanism is known as the competing risks/failure model, which has been studied extensively in the literature (see, for example, Wu et al, Chen et al, Bayramoglu, etc.). Most of the previous studies assumed that the failure causes are independent of each other, even when they have some dependent relationships.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for constant‐stress accelerated life tests with dependent competing risks from Marshall‐Olkin bivariate exponential distribution

Bai

Shi

Liu

et al. 2020

Quality & Reliability Eng

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This paper considers a constant‐stress accelerated dependent competing risks model under Type‐II censoring. The dependent structure between competing risks is modeled by a Marshall‐Olkin bivariate exponential distribution, and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability function under the normal usage condition at mission time are obtained by using the maximum likelihood estimation method and the bootstrap sampling technique. Moreover, the pivotal quantities based estimation are adopted to estimate the model parameters and the generalized confidence intervals. As a comparison, we also consider the Bayes estimation and the highest posterior density credible intervals for the model parameters based on conjugate priors and importance sampling method, respectively. To illustrate the proposed methodology, a Monte Carlo simulation is used to study the performances of different estimation methods. Finally, a dataset is analyzed for illustrative purpose and a comparison with the original results is also given.

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“…The reliability function of a ( r , s ) -out-of- n binary system is studied in Bayramoglu. 28 Therefore, equation (5) can be easily obtained for a multi-state system by Bayramoglu 28 for r = 1 and s = 1 . To find the reliability functions for ( 1 , 1 ) -out-of- n multi-state system in different states, let’s first give the reliability functions of the components for different states obtained based on the state probabilities of the components. Equations (6) and (7), which are probabilities of the first and the second components of the units being at states “ 2 ” and at state “ 1 ” or above are computed respectively by solving equation (3) (see Liu and Kapur 3 ).…”

Section: Description Of a (Rs)-out-of-n Multi-state System And Its Performance Evaluationmentioning

confidence: 99%

“…The reliability evaluation problem of a ( r , s ) -out-of- n system, a system of n lines which functions under the requirement that at least r components A i and at least s components B i functions, is considered in the study of Bayramoglu. 28 This type of modeling is also useful in shock models. In Omey and Vesilo, 29 they defined a bivariate shock model receiving different types of damages in the context of ( r , s ) -out-of- n system structure.…”

Section: Introductionmentioning

confidence: 99%

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

Iscioglu

Erem

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The performance evaluation of a system having n identical units, each of which consists of two components has been successfully discussed in binary-state reliability analysis. In this paper, we study the performance evaluation of a multi-state system based on bivariate order statistics. The multi-state system consists of n independent and identical units, each having two components. The components of each unit are assumed to be s-dependent. However, the units work s-independently with each other. The system and each component of each unit having three performance levels “0 (failure), 1 (partially working) and 2 (completely working)” are considered. The degradation of the components follows Markov Process and also Farlie-Gumbel-Morgenstern distribution is used to model the s-dependence of the components. The reliability analysis of a multi-state k-out-of- n system are evaluated under the assumptions. Some dynamic performance measures for the system such as the mean residual and mean past lifetime functions based on bivariate order statistics are also evaluated. The performance of the system is especially examined for different values of s-dependence parameter, the degradation rates and different number of units for the system. The results are supported with some numerical examples and graphical representations.

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Reliability and Mean Residual Life of Complex Systems With Two Dependent Components Per Element

Cited by 20 publications

References 39 publications

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Statistical inference for constant‐stress accelerated life tests with dependent competing risks from Marshall‐Olkin bivariate exponential distribution

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

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