Fault diagnosis method for unmanned aerial vehicle gyroscope

Abstract

The invention discloses a fault diagnosis method for an unmanned aerial vehicle gyroscope. The fault diagnosis method comprises the steps that the proximity of a sample and an expected value is calculated on the basis of a fuzzification technique, a sample membership degree factor is designed according to the proximity probability and configured into a mathematical model representing a support vector machine, and the influences generated by the sample distribution unbalance and the noise characteristic are eliminated; support vector machine training is conducted in an off-line mode, decision function parameters of an optimal interval classification face for distinguishing a positive sample and a negative sample are acquired, when an unmanned aerial vehicle flies, attitude signals output by an airborne sensor gyroscope are collected in real time, feature vectors of the signals are extracted by adopting a db4 wavelet basis decomposition method, normalized and then input to a decision function of the optimal interval classification face, a category label of a to-be-decomposed signal is calculated in real time, and the fault condition of the gyroscope is recognized. According to the diagnosis method, the biasing degree of a fault classification hyperplane is significantly decreased, the fault diagnosis precision is improved, the calculation amount is low, and the on-line real-time performance of a fault diagnosis algorithm is achieved.

Description

technical field [0001] The invention relates to a fault diagnosis of a sensor, in particular to a fault diagnosis method for a gyroscope of an unmanned aerial vehicle. Background technique [0002] With the advantages of UAV 3D (Dull: boring, Dirty: harsh environment, Dangerous: dangerous) mission execution advantages and low price advantages, UAVs are widely used in military reconnaissance, target positioning, electronic warfare, missile interception, etc. and civilian fields such as emergency rescue and disaster relief, inspection of power lines and oil and gas pipelines, anti-terrorism and stability maintenance, and aerial photography. [0003] UAV platforms in the field of military applications are often equipped with multi-type jamming equipment such as TACAN and radar; The navigation signal, the radiation signal generated by the engine operation, and the complex airborne electromagnetic environment produce uncertain interference on the output signal of the gyroscope, ...

AI Technical Summary

Problems solved by technology

However, this method does not discuss the influence of sample data set imbalance and noise singularity on the accuracy of fault diagnosis.

During the training process of support vector machine, the imbalance of positive and negative sample data sets and noise interference will lead to the hyperplane bias of the optimal classification decision function and reduce the accuracy of fault diagnosis

Images