Abstract
With the rapid development of more electric aircraft (MEA) in recent years, the aviation electric power system (AEPS) has played an increasingly important role in safe flight. However, as a highly reliable system, because of its complicated flight conditions and architecture, it often proves significant uncertainty in its failure occurrence and consequence. Thus, more and more stakeholders, e.g., passengers, aviation administration departments, are dissatisfied with the traditional system reliability analysis, in which failure uncertainty is not considered and system reliability probability is a constant value at a given time. To overcome this disadvantage, we propose a new methodology in the AEPS reliability evaluation. First, we perform a random sampling from the probability distributions of components’ failure rates and compute the system reliability at each sample point; after that, we use variance, confidence interval, and probability density function to quantify the uncertainty of system reliability. Finally, we perform the new method on a series–parallel system and an AEPS. The results show that the power supply reliability of AEPS is uncertain and the uncertainty varies with system time even though the uncertainty of each component’s failure is quite small; therefore it is necessary to quantify system uncertainty for safer flight, and our proposed method could be an effective way to accomplish this quantization task.
Highlights
Motived by the demand for greener, more efficient, more flexible, and safer flight, the aircraft industry has seen tremendous progress in the efforts of moving towards more electric aircraft (MEA) [1,2,3]
As the traditional method for system reliability analysis cannot capture the uncertainty at a system level which caused by epistemic and aleatory uncertainty, we are committed to finding a method for quantifying and computing the system reliability uncertainty that propagates from component failure uncertainty
Unlike traditional methods using constant component failure rate, the impact of component failure uncertainty on the whole system reliability is quantified in our work
Summary
Motived by the demand for greener (less gas emission and fuel consumption), more efficient, more flexible, and safer flight, the aircraft industry has seen tremendous progress in the efforts of moving towards more electric aircraft (MEA) [1,2,3]. To make these above methods feasible in practice, References [14,19,20,21] put forward practical algorithms for applying minimal cut set, fault tree, minimal path set, and Bayesian network All these works compute system reliability by integrating all the components into an equivalent graph and use constant failure rate for each component without considering the fact that failure rate may vary with loading conditions, weather conditions, pilot experience, etc.
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