Unmanned aerial vehicle fault prediction method based on deep neural network

Abstract

The invention provides an unmanned aerial vehicle fault prediction method based on a deep neural network, relates to the technical field of unmanned aerial vehicle fault early warning, and can realizefault early warning of unmanned aerial vehicle equipment for massive flight data of an unmanned aerial vehicle. The method comprises the steps of S1, extracting historical operation data of unmannedaerial vehicle equipment, and performing preprocessing and clustering analysis to obtain a training data set; S2, inputting the training data set into a deep neural network model for training; S3, acquiring real-time operation data of the unmanned aerial vehicle equipment, and preprocessing the real-time operation data; S4, inputting the preprocessed real-time operation data into a trained deep neural network model to obtain a result of whether the equipment is healthy or not; and S5, scoring a result obtained in the step S4 by utilizing the health degree measurement model, judging whether theresult is within a threshold range, if the result is within the threshold range, not outputting the result, and if the result is not within the threshold range, carrying out early warning. The methodprovided by the invention is suitable for the fault early warning process of the unmanned aerial vehicle equipment.

Description

【Technical field】 [0001] The invention relates to the technical field of UAV fault early warning, in particular to a UAV fault prediction method based on a deep neural network. 【Background technique】 [0002] As a high-tech weapon system, unmanned aerial vehicle (UAV) has received more and more attention for its failure prediction and maintenance support. UAV maintenance support technology has continued to evolve, with event-dominated maintenance or time-dependent maintenance being replaced by condition-based maintenance. [0003] Failure prediction is of great significance to UAV maintenance support. The readiness of failure prediction enables UAVs to detect hidden faults in time during and before flight, and avoid losses and major incidents caused by unplanned failures. The key is fitting / regression analysis of high-dimensional multivariate data. Data fitting / regression analysis is a predictive modeling technique that studies the relationship between a dependent variable...

AI Technical Summary

Problems solved by technology

For big data analysis, massive data leads to huge memory overhead, and it is unrealistic to directly find an analytical solution at this time

The number of data points monitored by UAV flight data is large, which is a typical massive high-dimensional nonlinear distribution data. Traditional methods face challenges in dealing with this type of problem.

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