A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Yuan, Xian‐Xun; Pandey, Mahesh D.

doi:10.1016/j.ress.2008.06.013

Cited by 90 publications

(24 citation statements)

References 5 publications

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“…Firstly, one of the most widely used methods to model the degradation data is the General Path Model, in which the maximum likelihood estimation, Bayesian estimation are usually utilized to estimate the general path model and the distribution function of failure time respectively [14][15] . Then, the second method is employing the stochastic process including the Markov process, Brownian motion and Gamma process to perform the degradation analysis [16][17] .…”

Section: The Degradation Analysismentioning

confidence: 99%

Reliability analysis for the competing failure with probabilistic failure threshold value and its application to the k-out-of-n systems

Yuan

Li²,

He³

et al. 2015

J. Shanghai Jiaotong Univ. (Sci.)

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A method for reliability analysis of the competing failure with the probabilistic failure threshold value not the fixed threshold value is presented, which involves the random shocks and the degradation is independent and dependent respectively. Specifically, for the dependent condition, the effect due to the random shocks on the degradation is considered with a damage factor. In addition, the dependent competing failure model is applied to the reliability analysis of the k-out-of-n systems. Finally, two studied cases are presented to illustrate the proposed method, and the results show the proposed method is reasonable.

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Section: The Degradation Analysismentioning

confidence: 99%

Reliability analysis for the competing failure with probabilistic failure threshold value and its application to the k-out-of-n systems

Yuan

Li²,

He³

et al. 2015

J. Shanghai Jiaotong Univ. (Sci.)

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“…The point and interval estimation of the reliability indices are shown in Table 2 and the reliability curve is shown in Figure 2. Reliability R(t) 90% intervals Figure 2 Reliability curve of the Ni-Cd battery based on pseudo life method Reliability Assessment based on random effect regression method The random effect regression method assumes the parameters of the degradation model is a random vector following the multi-dimensional normal distribution, which can describe the item-toitem variations as result of difference in materials and manufacturing processes [10].…”

Section: Reliability Assessment Of Ni-cd Battery Reliability Assessmementioning

confidence: 99%

Reliability Assessment of Satellite Ni-Cd Battery based on the End of Discharge Voltage

Duan¹,

Jin²,

Jian³

et al. 2015

Proceedings of the 2015 International Power, Electronics and Materials Engineering Conference

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Abstract. Ni-Cd battery is the key component of the satellite energy system with the characteristics of long life and high reliability, and few or no failure occurs in its traditional life testing. In this paper, based on the performance degradation modeling method, we establish a dynamic degradation model to capture the evolution of the end of discharge voltage (EODV), which is a function of cycle number and temperature. Two methods are utilized to assess the reliability of Ni-Cd battery. Results show that the random effect regression method obtains a more conservative reliability estimation result that the pseudo life based method does.

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“…Alternative approaches to modeling the degradation of the state-of-the-system include Brownian motion and other stochastic processes (Doksum & Hoyland, 1992;Liao, Elsayed, & Chan, 2006) and Bayesian methods for remaining life distributions (Gebraeel, Lawley, Li, & Ryan, 2005), among others (Shiau & Lin, 1999;Yuan & Pandey, 2009). The approach used here describes the state of the system, x(t), with a continuous fitted degradation model.…”

Section: Degradation Modelsmentioning

confidence: 99%

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Barker¹

2013

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The aim of this research work is to develop a framework for making coordinated acquisitions decisions, integrating: (i) schedules of maintenance based on the condition of system components, and (ii) trigger acquisition of multiple vendor maintenance, repair, and overhaul (MRO) supplies and services. To do so, we develop a multi-objective optimization maintenance/acquisition scheduling algorithm where tradeoffs are made between two competing objectives: maximizing conditionbased component reliability while maximizing the sensitivity of condition-based reliability to changes in the component's condition (e.g., degradation). This scheduling algorithm integrates with an acquisition algorithm to addresses vendor lead-time. Case studies broadly inspired by Tinker Air Force Base, the largest Air Force MRO hub in the US, in Oklahoma City, OK, illustrate the framework. Keywords

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A nonlinear mixed-effects model for degradation data obtained from in-service inspections

Cited by 90 publications

References 5 publications

Reliability analysis for the competing failure with probabilistic failure threshold value and its application to the k-out-of-n systems

Reliability analysis for the competing failure with probabilistic failure threshold value and its application to the k-out-of-n systems

Reliability Assessment of Satellite Ni-Cd Battery based on the End of Discharge Voltage

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

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