CNN-Bagging-based fault diagnosis method for UAV bearing

A fault diagnosis and unmanned aerial vehicle technology, which is applied in computer parts, mechanical parts testing, machine/structural parts testing, etc., can solve the problem that the model accuracy is easily affected by the cardinality, the model training efficiency and fault diagnosis accuracy are low, and there is no Considering the correlation of signal faults and other issues to achieve the effect of improving accuracy and generalization ability, which is conducive to real-time performance and fast training speed

Active Publication Date: 2019-10-08
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Researchers usually input one-dimensional vibration time-domain signals into the CNN model for fault diagnosis. This input form does not consider the internal correlation of signal faults, resulting in low model training efficiency and low fault diagnosis accuracy.
In response to this problem, the present invention proposes to perform structural conversion on the signal based on a certain arrangement base to fo

Method used

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  • CNN-Bagging-based fault diagnosis method for UAV bearing
  • CNN-Bagging-based fault diagnosis method for UAV bearing
  • CNN-Bagging-based fault diagnosis method for UAV bearing

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0048] Example

[0049] For the convenience of description, first explain the relevant professional terms appearing in the specific implementation:

[0050] EMD (Empirical Mode Decomposition): Empirical Mode Decomposition;

[0051] VMD (variational mode decomposition) variational mode decomposition;

[0052] Bagging (bootstrap aggregating): Self-service sampling aggregation;

[0053] CNN: Convolutional Neural Network;

[0054] ANN (Artificial Neutral Network): artificial neural network;

[0055] RNN (Recurrent Neural Networks): Recurrent Neural Network;

[0056] figure 1 It is the flow chart of the UAV bearing fault diagnosis method based on CNN-Bagging of the present invention.

[0057] In this embodiment, as figure 1 As shown, a CNN-Bagging-based UAV bearing fault diagnosis method of the present invention includes the following steps:

[0058] S1, get the signal data set

[0059] Obtain all bearing signals in the UAV to form a signal data set F={f (i) |i∈[1,m]}, f (i) Indicates the signal g...

Example Embodiment

[0096] Instance

[0097] Suppose a UAV has n bearings, f1, f2,..., fn. For f1, the bearing signal of f1 is decomposed into time-domain signal S1 and time-frequency signal F1 based on the variational modal decomposition (VMD), and then the fault diagnosis of f1 is performed through the integrated model CNN. Based on the above process, the fault diagnosis of bearings f2...fn is performed, and the diagnosis process is as follows: Figure 7 As shown, the fault condition of the bearing is finally judged according to the diagnosis result of each bearing

[0098] The model judges the parameters accuracy Acc, precision rate P, recall rate R, F1 and calculation speed and other indicators.

[0099]

[0100]

[0101]

[0102]

[0103] Let S N And F N Indicates the number of time-domain models and time-frequency domain models respectively, and the final test results are shown in Table 1.

[0104] index parameterSN

[0105] Table 1

[0106] Such as Figure 8 As shown, the accuracy of time-domai...

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Abstract

The invention discloses a CNN-Bagging-based fault diagnosis method for a UAV bearing. A bearing signal is collected, the bearing signal is preprocessed, and a time-domain signal and a time-frequency-domain signal are extracted; a time domain weak classification module and a time-frequency-domain weak classification module are constructed based on an integrated learning algorithm according to the atime-domain signal and the time-frequency-domain signal; and then a membership probability value of a to-be-detected unmanned aerial vehicle bearing signal is predicted based on the time domain weakclassification module and the time-frequency-domain weak classification module. Therefore, the fault diagnosis of the UAV bearing is realized.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of unmanned aerial vehicle systems, and more specifically relates to a fault diagnosis method for unmanned aerial vehicle bearings based on CNN-Bagging integrated learning. Background technique [0002] UAV technology is changing with each passing day, and all kinds of UAVs play a huge role in the military field. The bearing failure of the aero-engine is the main factor causing the failure of the UAV, which can directly affect the reliability and health of the engine. Therefore, the bearing fault diagnosis of UAV is an important research topic. There are various types of faults in UAV bearings. How to identify the types of bearing faults with high precision is of great significance to the stability and reliability of UAV systems. In addition, the space attitude of UAV flight often leads to various bearing stress environments, so there are high requirements for the generalization ability ...

Claims

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Application Information

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IPC IPC(8): G01M13/045G06K9/62
CPCG01M13/045G06F18/241G06F18/214
Inventor 程玉华殷春马浩鹏彭威黄雪刚陈凯时晓宇周静
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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