Airship propeller reliability estimation method based on chaotic initialization SSA-BP neural network

Abstract

The invention discloses an airship propeller reliability estimation method based on a chaos initialization SSA-BP neural network. The method comprises the following steps: determining main factors influencing blade strain of a propeller under a design parking working condition; constructing a training / testing input data set of the chaotic initialization SSA-BP neural network; solving the strain value of the maximum strain position of the propeller under the working condition of taking the input data set as the working condition; establishing a chaotic initialization SSA-BP neural network model; normal distribution discretization is carried out on the designed flight height and rotating speed of the propeller under the mission profile according to the 3 sigma principle, a new input data set is obtained, and normal distribution discretization is carried out on the allowable strain value of the propeller according to the variable coefficient of the allowable strain value according to the 3 sigma principle; and solving the failure rate of the airship propeller and the MTBF (mean time of failure). According to the method, the situation of falling into a local optimal solution is effectively avoided, the prediction precision and the prediction efficiency are improved, the reliability of the propeller can be quickly estimated, and the method has great engineering value.

Description

technical field [0001] The invention belongs to the technical field of reliability evaluation, and in particular relates to a method for evaluating the reliability of an airship propeller. Background technique [0002] The reliability estimation of composite propellers is a key content in the safety index of high-altitude airships, and the mean time between failures (MTBF) is a kind of ability that reflects the propeller's ability to maintain its thrust without damage within a specified time. Due to the particularity of its operating conditions, high-altitude airships are required to fly for a long time within the specified working altitude without landing. For this special unmanned aerial vehicle, it is very important for airships to accurately estimate the MTBF of composite material propellers. The design and technical improvements of the design also help a lot. [0003] At present, there are two main methods for estimating MTBF. One is through the continuous rotation tes...

AI Technical Summary

Problems solved by technology

There is another problem here. Airship flight height and propeller speed are often not located exactly at the design point. It can be understood as a normal distribution based on the design point. There are many working conditions that need to be calculated, and the finite element calculation is time-consuming. The strain varies with the It is not easy to find the change law of flight altitude and speed, so BP neural network can be added to estimate the strain, and then estimate the MTBF

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