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Bearing fault classification method based on CNN and Adaboost

A bearing and weak classification technology, applied in the field of UAV bearing fault classification based on CNN and Adaboost integrated learning, can solve the problem that the model accuracy is easily affected by the cardinality, does not consider the signal fault correlation, model training efficiency and fault diagnosis accuracy Low-level problems, to achieve the effect of improving recognition accuracy and prosperous ability, conducive to real-time performance, and flexible structure

Active Publication Date: 2019-10-08
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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 form a grid input form, but the existing problem is that the accuracy of the model is easily affected by the base
The most representative of the Boosting family algorithms is Adaboost. At present, in the aspect of UAV fault diagnosis, there is no public related literature on the diagnosis method that combines deep learning and Adaboost integrated learning. Therefore, this high-precision and strong The research of fault diagnosis algorithm with generalization ability is of great significance

Method used

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  • Bearing fault classification method based on CNN and Adaboost
  • Bearing fault classification method based on CNN and Adaboost
  • Bearing fault classification method based on CNN and Adaboost

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Experimental program
Comparison scheme
Effect test

Embodiment

[0066] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0067] EMD (Empirical Mode Decomposition): Empirical Mode Decomposition;

[0068] VMD (variational mode decomposition) variational mode decomposition;

[0069] Adaboost (Adaptive Boosting): A type of integrated learning boosting;

[0070] CNN: convolutional neural network;

[0071] ANN (Artificial Neutral Network): artificial neural network;

[0072] RNN (Recurrent Neural Networks): recurrent neural network;

[0073] figure 1 It is a flow chart of the present invention's bearing fault classification method based on CNN and Adaboost.

[0074] In this example, if figure 1 Shown, a kind of bearing fault classification method based on CNN and Adaboost of the present invention comprises the following steps:

[0075] S1. Obtain signal data set

[0076] Obtain all bearing signals in the UAV to form a signal data set F={f (i) |i∈[1,m]}, f (i...

example

[0131] Suppose a UAV has n bearings, which are f1, f2,..., fn. For f1, firstly, the f1 bearing signal is decomposed into time-domain signal S1 and time-frequency signal F1 based on variational mode decomposition (VMD), and then the fault diagnosis of f1 is carried out through the integrated model CNN+Adaboost. Then based on the above process, make fault diagnosis for bearings f2...fn respectively, the diagnosis process is as follows Figure 7 As shown in Fig. 1, the fault condition of the bearing is finally judged according to the diagnosis result of each bearing.

[0132] Model evaluation parameters accuracy Acc, precision rate P, recall rate R, F1 and calculation speed and other indicators.

[0133]

[0134]

[0135]

[0136]

[0137] Let S N and F N Represent the number of time-domain models and time-frequency domain models respectively, and the final test results are shown in Table 1.

[0138] index parameter S N

F N

ACC(%) P(％) R(％) ...

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Abstract

The invention discloses a bearing fault classification method based on CNN and Adaboost. 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 the time domain signal and the time-frequency domain signal; and then the time-domain weak classification module and the time-frequency-domain weak classification module are integrated and a membership probability value of a to-be-detected unmannedaerial vehicle bearing signal is predicted by using the integrated classification model. Therefore, the classification of UAV bearing faults 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 classification method for unmanned aerial vehicle bearings based on integrated learning of CNN and Adaboost. 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 generaliza...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045G06N20/20G06N3/04G06N3/08
CPCG01M13/045G06N20/20G06N3/08G06N3/045
Inventor 殷春程玉华彭威陈凯黄雪刚马浩鹏周静杨晓
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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