Mission Reliability Assessment of Space TT&amp;C System by Discrete Event System Simulation

Yang, Xiao‐Song; Xiao-yue, WU

doi:10.1002/qre.1546

Cited by 22 publications

(7 citation statements)

References 13 publications

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Section: Related Workmentioning

confidence: 99%

“…PMSs involve multiple tasks or phases during the mission, which abound in many practical applications such as aerospace, airborne weapon systems, sensor networks, and military units. [8][9][10] A PMS often has to accomplish a different task and may be subject to different dependability requirements and environment conditions during each phase. Thus, the system configuration, failure or success criteria, and component failure behavior can be dynamic from phase to phase, which makes the reliability analysis of PMSs difficult.…”

Section: Phased Mission Systems (Pms)mentioning

confidence: 99%

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A hierarchical combinatorial reliability model for smart home systems

Zhao

Xing

Zhang

et al. 2017

Quality & Reliability Eng

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As an application of the Internet of Things, smart home systems have received significant attentions in recent years due to their precedent advantages, eg, in ensuring efficient electricity transmission and integration with renewable energy. This paper proposes a hierarchical and combinatorial methodology for modeling and evaluating reliability of a smart home system. Particularly, the proposed methodology encompasses a multi-valued decision diagram-based method for addressing phased-mission, standby sparing, and functional dependence behaviors in the physical layer; and a combinatorial procedure based on the total probability theorem for addressing probabilistic competing failure behavior with random propagation time in the communication layer. The methods are applicable to arbitrary types of time-to-failure and time-to-propagation distributions for system components. A detailed case study of an example smart home system is performed to demonstrate applications of the proposed method and effects of different component parameters on the system reliability. (IoT) is an internetworking of myriad of "smart" objects and devices augmented with sensors and actuators (such as cell phones, smart appliances, electrical devices, etc.). [1][2][3] In other words, an IoT smart system incorporates sensing, actuation, and control functions for monitoring and analyzing a situation, and Abbreviations: EMS, Energy management system; FCE, Failure competition event; FDEP, Functional dependence; HSP, Hot spare; IFP, Isolation factor pair; IoT, Internet of Things; LF, Local failure; LS, Local sensing failure; LT, Local transmitting failure; MDD, Multi-valued decision diagram; PDEP, Probabilistic function dependence; PDF, Probability density function; PFDC, Probabilistic function dependence case; PFGE, Propagated failure with global effect; PMS, Phased mission system; PT, Propagation time. Notations: h, Phase number; f iP (t), f iLS (t), f iLT (t), PDF of time-to-PFGE/LS/LT of component i; f iPT (t), PDF of time-to-propagation of component i; FCE j , Failure competition event j, j = 1,2,3; ψ i , The failure function of the i-th phase in the physical layer; ψ xi;h , Unreliability of component i at the end of h; I, Union of set of components causing PFGE and set of components being isolated under an FCE; P FCEj t ð Þ, Probability of no PFGE to components in I; q iLS (t), q iLT (t), Occurrence probability of LS, LT to component i; q iP (t), Occurrence probability of PFGE to component i; q iS (t), q iT (t), Occurrence probability of LS, LT given that no PFGE to i; Q FCEj t ð Þ, Failure probability given that no PFGE happens to components in I; T h , Duration of phase h; x i = 0, Component i functions in all the phases; x i = h, Component i fails during h given that i is operational at the beginning of h; X iP , X iLS , X iLT , Event that component i has a PFGE, LS, LT; X iPT , Event that the propagation time is less than the time difference between the relay LT and the component i PFGE; U comm (t), Unreliability of the communicat...

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Section: Related Workmentioning

confidence: 99%

Section: Phased Mission Systems (Pms)mentioning

confidence: 99%

A hierarchical combinatorial reliability model for smart home systems

Zhao

Xing

Zhang

et al. 2017

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…These methods are often used for the reliability assessment of large systems because unlike the non‐simulation‐based methods, the size of reliability problem is essentially independent of the number of system components. Various classifications of simulation‐based methods have been proposed in the literature as follows: Cross‐Entropy Methods; Discrete Event Simulation; Fuzzy Bayesian System Reliability; Line Sampling; Monte Carlo Simulation Method (]); Subset Simulation; Weighted Average Simulation …”

Section: Introductionmentioning

confidence: 99%

“…15,16 These methods are often used for the reliability assessment of large systems because unlike the non-simulation-based methods, the size of reliability problem is essentially independent of the number of system components. Various classifications of simulation-based methods have been proposed in the literature as follows: Cross-Entropy Methods 17 ; Discrete Event Simulation 18 ; Fuzzy Bayesian System Reliability 19 ; Line Sampling 20 ; Monte Carlo Simulation Method ( 21 ]); Subset Simulation 22 ; Weighted Average Simulation. 23 Although the two categories of methods, as described above, have been successfully developed and applied for reliability assessment of complex systems, these methods lack the capability of achieving a closer adherence to INs.…”

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confidence: 99%

Exact reliability evaluation of infrastructure networks using graph theory

Zarghami

Gunawan

Schultmann

2019

Quality & Reliability Eng

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Reliability analysis of Infrastructure Networks (INs) is gaining recognition in the research literature. However, most of the work on reliability evaluation of INs have focused on simulation analysis and, therefore, unable to calculate the exact reliability. Additionally, these methods lack the capability of achieving a closer adherence to INs. The presented paper aims at filling these gaps by simplifying the process of computing the exact reliability of an IN through the decomposition of the network into a set of series and parallel configuration of its elements. In exemplifying the method, an illustrative example is presented and a brief discussion on the usefulness and limitation of the method is described.

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“…The simulation method is a commonly used method in engineering. Yang et al [8] used the Petri net simulation method to analyze the reliability of TT&C missions and statistically analyzed key TT&C resources that would cause failures of missions. Due to the high reliability and short task window in aerospace engineering, the direct simulation method requires a large number of simulation times to yield an acceptable reliability simulation result.…”

Section: Introductionmentioning

confidence: 99%

Simulation Method for Mission Reliability Assessment of Space Telemetry Tracking and Command System with Dynamic Redundancy

Wu¹

2019

IJPE

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The mission reliability of space telemetry tracking and command (TT&C) systems directly determines whether the space mission can be carried out successfully. In actual situations, some missions of TT&C systems may just be required to be completed before a given time, rather than be performed in a specified time window. Taking advantage of this feature, mission failures in the originally arranged time period could then be re-executed later according to the rescheduling algorithm. The resources to be used and the time to be executed are two kinds of uncertainties that exist in rescheduling. This paper treats them as dynamic redundancy. The existing evaluation methods are insufficient to evaluate the mission reliability of system under this condition. This paper proposes a heuristic rule based on the system operation-principle to obtain the basic scheduling plan of the mission and then designs a reactive scheduling strategy to deal with mission failures during the execution process. Based on this scheduling architecture, a basic scheduling plan combined with a fault handling strategy can be obtained. A Monte Carlo method is used to simulate the components' failure and repair, and a discrete event simulation process is used to simulate the possible execution options during the execution process. A collection of altered execution options combined with the execution result of the mission can be gathered after a huge amount of simulation. The approximate mission reliability of the system can then be assessed through a statistic reference to this collection.

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Mission Reliability Assessment of Space TT&C System by Discrete Event System Simulation

Cited by 22 publications

References 13 publications

A hierarchical combinatorial reliability model for smart home systems

A hierarchical combinatorial reliability model for smart home systems

Exact reliability evaluation of infrastructure networks using graph theory

Simulation Method for Mission Reliability Assessment of Space Telemetry Tracking and Command System with Dynamic Redundancy

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