method for analyzing and evaluating a turbine engine based on a Markov dynamic fault tree

Abstract

The invention relates to a method for evaluating a turbine engine based on a Markov dynamic fault tree, and belongs to the field of aviation. By combining the accurate failure order of the Markov model and the intuitiveness theory of the logic of the fault tree, safety modeling and analysis evaluation are carried out on the fuzzy system with dynamic failure and uncertainty related to time at the same time. The dynamic logic in the fault tree is converted into the Markov model, and the calculation problem of the dynamic logic gate can be rapidly solved. The combination state of the dynamic logic input events is used as the basic state of the Markov model, and the state transition rate of the Markov model is set as the failure rate of the input events, so that the Markov model can express the dynamic logic gates.

Description

technical field [0001] The invention relates to a Markov-based dynamic fault tree analysis and evaluation method for a turbine engine, which belongs to the field of aviation. Background technique [0002] The turbofan engine is known as a shining pearl in the aviation industry, and its advanced nature is an important symbol to measure a country's industrial capability level. In the process of developing an engine, system safety analysis and evaluation are essential, and the engine control system, as a core subsystem of the engine, is an important part of evaluating the safety of the control system. Therefore, the research on the control system evaluation method without delay. [0003] System fault tree analysis and evaluation is often used for system reliability. However, it must rely on analysis and evaluation personnel to manually analyze and evaluate the system according to the process. Therefore, the efficiency of analysis and evaluation is low, the workload is heavy, a...

AI Technical Summary

Problems solved by technology

[0003] System fault tree analysis and evaluation is often used for system reliability. However, it must rely on analysis and evaluation personnel to manually analyze and evaluate the system according to the process. Therefore, the efficiency of analysis and evaluation is low, the workload is heavy, and data extraction is relatively mechanical. The data to be analyzed and evaluated are also added to the content, which increases the workload of the subsequent steps and has redundancy and limitations.

The traditional Markov analysis and evaluation method can calculate the probability of a maintenance-capable system with multiple degraded states, but all items are statistically independent, and it is necessary to understand the various probabilities of state changes, thus reducing the overall analysis and evaluation to a certain extent. process adequacy

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