Reliability Analysis of Load-Sharing Systems Subject to Dependent Degradation Processes and Random Shocks

In this article, a new multiple competing failure models proposed, in which multiple degradation processes and random shocks are considered. The shocks have a significant impact on system reliability, and the system reliability under not considering the impact of shocks is much higher than the impact of shocks is considered. In addition, the influences of the correlation among different degradation processes are also considered in this article. We can find that the reliability with considering the dependent case is higher than the independent case reliability. Thus the dependence of the two degradation processes is non-negligible. A numerical example with reliability analysis and sensitivity analysis is discussed to illustrate the proposed the new model.

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Reliability analysis and state transfer scheduling optimization of degrading load-sharing system equipped with warm standby components

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211001713 ◽

2021 ◽

pp. 1748006X2110017

Author(s):

Sheng-Jia Ruan ◽

Yan-Hui Lin

Keyword(s):

Reliability Analysis ◽

System Reliability ◽

High Reliability ◽

Quadrature Rule ◽

Load Sharing ◽

Measurement Information ◽

Model Parameters ◽

Scheduling Optimization ◽

State Transfer ◽

Warm Standby

Standby redundancy can meet system safety requirements in industries with high reliability standards. To evaluate reliability of standby systems, failure dependency among components has to be considered especially when systems have load-sharing characteristics. In this paper, a reliability analysis and state transfer scheduling optimization framework is proposed for the load-sharing 1-out-of- N: G system equipped with M warm standby components and subject to continuous degradation process. First, the system reliability function considering multiple dependent components is derived in a recursive way. Then, a Monte Carlo method is developed and the closed Newton-Cotes quadrature rule is invoked for the system reliability quantification. Besides, likelihood functions are constructed based on the measurement information to estimate the model parameters of both active and standby components, whose degradation paths are modeled by the step-wise drifted Wiener processes. Finally, the system state transfer scheduling is optimized by the genetic algorithm to maximize the system reliability at mission time. The proposed methodology and its effectiveness are illustrated through a case study referring to a simplified aircraft hydraulic system.

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Dependability Analysis of Homogeneous Distributed Software/Hardware Systems

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539315500072 ◽

2015 ◽

Vol 22 (02) ◽

pp. 1550007

Author(s):

G. Vijayalakshmi

Keyword(s):

Reliability Analysis ◽

Distributed System ◽

Failure Time ◽

Load Sharing ◽

Accelerated Failure Time Model ◽

Time To Failure ◽

Critical Systems ◽

Time Model ◽

Distributed Software ◽

Dependability Analysis

With the increasing demand for high availability in safety-critical systems such as banking systems, military systems, nuclear systems, aircraft systems to mention a few, reliability analysis of distributed software/hardware systems continue to be the focus of most researchers. The reliability analysis of a homogeneous distributed software/hardware system (HDSHS) with k-out-of-n : G configuration and no load-sharing nodes is analyzed. However, in practice the system load is shared among the working nodes in a distributed system. In this paper, the dependability analysis of a HDSHS with load-sharing nodes is presented. This distributed system has a load-sharing k-out-of-(n + m) : G configuration. A Markov model for HDSHS is developed. The failure time distribution of the hardware is represented by the accelerated failure time model. The software faults are detected during software testing and removed upon failure. The Jelinski–Moranda software reliability model is used. The maintenance personal can repair the system up on both software and hardware failure. The dependability measures such as reliability, availability and mean time to failure are obtained. The effect of load-sharing hosts on system hazard function and system reliability is presented. Furthermore, an availability comparison of our results and the results in the literature is presented.

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Reliability analysis of load-sharing systems with spatial dependence and proximity effects

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.108284 ◽

2022 ◽

pp. 108284

Author(s):

Bodunrin Brown ◽

Bin Liu ◽

Stuart McIntyre ◽

Matthew Revie

Keyword(s):

Reliability Analysis ◽

Spatial Dependence ◽

Load Sharing ◽

Proximity Effects

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Reliability Analysis of Load-SharingK-out-of-NSystem Considering Component Degradation

Mathematical Problems in Engineering ◽

10.1155/2015/726853 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Chunbo Yang ◽

Shengkui Zeng ◽

Jianbin Guo

Keyword(s):

Reliability Analysis ◽

Failure Rate ◽

Load Sharing ◽

Performance Degradation ◽

Random Component ◽

Rate Model ◽

Successful Operation ◽

Degradation Model ◽

System Requirement ◽

Degradation Effect

TheK-out-of-Nconfiguration is a typical form of redundancy techniques to improve system reliability, where at leastK-out-of-Ncomponents must work for successful operation of system. When the components are degraded, more components are needed to meet the system requirement, which means that the value ofKhas to increase. The current reliability analysis methods overestimate the reliability, because using constantKignores the degradation effect. In a load-sharing system with degrading components, the workload shared on each surviving component will increase after a random component failure, resulting in higher failure rate and increased performance degradation rate. This paper proposes a method combining a tampered failure rate model with a performance degradation model to analyze the reliability of load-sharingK-out-of-Nsystem with degrading components. The proposed method considers the value ofKas a variable which is derived by the performance degradation model. Also, the load-sharing effect is evaluated by the tampered failure rate model. Monte-Carlo simulation procedure is used to estimate the discrete probability distribution ofK. The case of a solar panel is studied in this paper, and the result shows that the reliability considering component degradation is less than that ignoring component degradation.

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