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Bearing fault diagnosis method based on CNN-Stacking

A fault diagnosis and bearing technology, applied in the field of fault diagnosis of unmanned aerial vehicle systems, can solve the problems that the model accuracy is easily affected by the cardinal number, does not consider the correlation of signal faults, the model training efficiency and the fault diagnosis accuracy are low, etc., so as to improve the identification Accuracy and generalization ability, beneficial to real-time performance, and the effect of improving robustness

Active Publication Date: 2019-10-15
UNIV OF ELECTRONIC 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
At present, in terms of fault diagnosis of UAVs, there is no published relevant literature on the diagnosis method combining deep learning and integrated learning. Therefore, the research of this fault diagnosis algorithm with high precision and strong generalization ability is of great significance

Method used

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  • Bearing fault diagnosis method based on CNN-Stacking
  • Bearing fault diagnosis method based on CNN-Stacking
  • Bearing fault diagnosis method based on CNN-Stacking

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0050] For the convenience of description, the relevant technical terms appearing in the specific implementation are explained first:

[0051] EMD (Empirical Mode Decomposition): Empirical Mode Decomposition;

[0052] VMD (variational mode decomposition) variational mode decomposition;

[0053] Stacking: stacking method;

[0054] CNN: convolutional neural network;

[0055] ANN (Artificial Neutral Network): artificial neural network;

[0056] RNN (Recurrent Neural Networks): recurrent neural network;

[0057] figure 1 It is a flow chart of the CNN-Stacking-based UAV bearing fault diagnosis method of the present invention.

[0058] In this example, if figure 1 Shown, a kind of CNN-Stacking-based UAV bearing fault diagnosis method of the present invention comprises the following steps:

[0059] S1. Obtain signal data set

[0060] S1.1. Obtain the signals of all bearings in the UAV to form an unbalanced data set F={f (i) |i∈[1,m]}, f (i) Indicates the signal generated by...

example

[0099]Suppose a UAV has n bearings, which are f1, f2,..., fn. For f1, 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. 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.

[0100] Model evaluation parameters accuracy Acc, precision rate P, recall rate R, F1 and calculation speed and other indicators.

[0101]

[0102]

[0103]

[0104]

[0105] 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.

[0106] index parameter S N

F N

ACC(%) P(％) R(％) F1 20 20 96.753 ...

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Abstract

The invention discloses an unmanned aerial vehicle bearing fault diagnosis method based on CNN-Stacking. According to the method, first, bearing signals are collected, then the bearing signals are preprocessed, and a time domain signal and a time-frequency domain signal are extracted; second, a time domain weak classification mode and a time-frequency domain weak classification mode are constructed through an integrated learning algorithm based on the time domain signal and the time-frequency domain signal respectively; and last, after cascade fusion of the time domain weak classification modeand the time-frequency domain weak classification mode, a membership probability value of a to-be-detected unmanned aerial vehicle bearing signal is predicted, and therefore unmanned aerial vehicle bearing fault diagnosis 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-Stacking 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): G01M13/045G06K9/62G06N3/04G06N3/08
CPCG01M13/045G06N3/08G06N3/045G06F18/241
Inventor 陈凯殷春况祥程玉华周静彭威马浩鹏张阔
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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