Monte-Carlo Simulation of Reliability of System with Complex Interconnections

Modern automotive systems must satisfy strict reliability requirements. Most real vehicle systems and safety-critical networks have complex interconnections. The sensitivities and probabilistic uncertainties of the reliability of systems with complex interconnections (SwCIs) can be investigated by Monte-Carlo Simulation (MCS). This paper focuses on the sensitivities and parametrical uncertainties of SwCIs’ reliability. The proposed method can be implemented in the investigation of the uncertainties of SwCI reliability, i.e., in the determination of critical system elements and the estimation of the required number of spare parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

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Publication Details

Journal
Vehicles
Published
2024-10-11
DOI
https://doi.org/10.3390/vehicles6040087
Primary Topic
Reliability and Maintenance Optimization
Type
article
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article

Monte-Carlo Simulation of Reliability of System with Complex Interconnections

László Pokorádi
Vehicles
Reliability and Maintenance Optimization
article

Monte-Carlo Simulation of Reliability of System with Complex Interconnections

László Pokorádi
article en

Abstract

Modern automotive systems must satisfy strict reliability requirements. Most real vehicle systems and safety-critical networks have complex interconnections. The sensitivities and probabilistic uncertainties of the reliability of systems with complex interconnections (SwCIs) can be investigated by Monte-Carlo Simulation (MCS). This paper focuses on the sensitivities and parametrical uncertainties of SwCIs’ reliability. The proposed method can be implemented in the investigation of the uncertainties of SwCI reliability, i.e., in the determination of critical system elements and the estimation of the required number of spare parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage.

VehiclesVol. 6(4)
Obuda University (HU)
Openalex Percentile: Top 79%
Reliability and Maintenance Optimization
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