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Rotating machinery small sample fault diagnosis method based on generative adversarial network

A technology for rotating machinery and fault diagnosis, applied in biological neural network models, neural learning methods, testing of mechanical parts, etc.

Pending Publication Date: 2022-02-25
TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The object of the present invention is to provide a kind of small-sample fault diagnosis method of rotating machinery based on generation confrontation network, thereby solving the aforementioned problems existing in the prior art

Method used

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  • Rotating machinery small sample fault diagnosis method based on generative adversarial network
  • Rotating machinery small sample fault diagnosis method based on generative adversarial network
  • Rotating machinery small sample fault diagnosis method based on generative adversarial network

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Embodiment

[0064] figure 2 It is an implementation flowchart of an embodiment of the small-sample fault diagnosis method of rotating machinery based on generative confrontation network in the present invention. Such as figure 2 As shown, the present invention is based on the specific steps of the rotating machinery small sample fault diagnosis method of generation confrontation network comprising:

[0065] S1: Use the vibration acceleration sensor to collect the vibration signal of the rotating machinery.

[0066] S2: Carry out data preprocessing on the collected vibration signals of rotating machinery, construct an original data set, and divide the original data set into a training set and a test set.

[0067] S3: Build a network model based on ACWGAN-GP-ARGMAX.

[0068] S4: Carry out model training on the ACWGAN-GP-ARGMAX-based network model, and integrate the trained network model in each health state to construct a diagnostic model.

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Abstract

The invention discloses a rotating machinery small sample fault diagnosis method based on a generative adversarial network. The method comprises the following steps: firstly, converting an acquired rotating machinery time domain signal into a frequency spectrum signal of which the characteristics are easy to observe through fast Fourier transform (FFT); and then, on the basis of the ACWGAN-GP, introducing an 'argmax' multi-classification thought so that an ACWGAN-GP-ARGMAX diagnosis model is obtained. According to the diagnosis model, a discriminator is endowed with classification and recognition capability, the classification and recognition capability of the discriminator is enhanced, and the accuracy and efficiency of fault diagnosis of the small sample of the rotating machinery can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of rotating machinery, in particular to a fault diagnosis method for small samples of rotating machinery based on generative confrontation network. Background technique [0002] Rotating machinery is widely used in aerospace, shipbuilding, wind power, petrochemical and other fields. It is one of the key components in industrial scenarios, and it is also a kind of equipment that is prone to failure. Most of the rotating machinery is a cyclic symmetrical structure, which is stable under normal working conditions. However, when the rotating machinery fails, its symmetry is broken, resulting in unstable performance and prone to major accidents. In order to avoid catastrophic failure of rotating machinery, it is of great significance to continuously monitor and diagnose its operating status. [0003] The operating status of rotating machinery can usually be detected and diagnosed by measuring...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01M13/00G01H17/00
CPCG06N3/086G01M13/00G01H17/00G06N3/045G06F2218/08G06F2218/12G06F18/24G06F18/214
Inventor 曹智伏洪勇王珂李振祥张俊华
Owner TECH & ENG CENT FOR SPACE UTILIZATION CHINESE ACAD OF SCI
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