Rotary machine axis trajectory recognition method based on deep learning

A technology of axis trajectory and rotating machinery, which is applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., and can solve the problem of low recognition accuracy of axis trajectory of rotating machinery

Active Publication Date: 2021-05-18
东方电气集团科学技术研究院有限公司 +1
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Problems solved by technology

[0005] In view of the deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a method for identifying the axis track of a rotating machine based on deep learning, which is used to solve the problem of low accuracy in identifying the track of the axis of a rotating machine and relying too much on descriptions. The problem of complex mathematical sign extraction

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  • Rotary machine axis trajectory recognition method based on deep learning
  • Rotary machine axis trajectory recognition method based on deep learning

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[0023] The features of the inventive concept and the method for realizing the inventive concept can be more easily understood through the detailed description in combination with the embodiments and the accompanying drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of various embodiments.

[0024] Such as figure 1 As shown, a method for identifying the axis trajectory of a rotating machinery based on deep learning, the steps include the following:

[0025] In operation S101 , collect shaft center track data of the rotating machinery under a fault to form a fault sample library; wherein the shaft center track data includes a shaft center track graph and a shape label corresponding to the shaft center track graph.

[0026] In operation S103, data enhancement is performed on the axis trajectory diagram, and an axis trajectory recognition model is constructed based on a deep ne...

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Abstract

The invention discloses a rotary machine axis trajectory recognition method based on deep learning. The method comprises the steps of firstly, collecting axis trajectory data under a fault of a rotary machine, obtaining an axis trajectory diagram and a shape label corresponding to the axis trajectory diagram, and forming a fault sample library, secondly, performing data enhancement on the axis trajectory diagram in the sample library, and then constructing an axis trajectory recognition model based on a deep neural network, and thirdly, collecting axis trajectory data during operation of the rotating machine in real time, conducting comparison diagnosis based on the constructed axis trajectory recognition model, determining the shape of the axis trajectory online, and then determining the fault type. According to the method, automatic axis trajectory recognition can be realized without depending on complex description mathematical feature extraction, and meanwhile, different view features of the axis trajectory are extracted by using convolution kernels of different sizes, so that the recognition precision is improved; in addition, the fault sample library can be updated according to real-time data, the recognition model is continuously optimized, and the function of self-perfecting and upgrading is achieved.

Description

technical field [0001] The present invention relates to the technical field of fault diagnosis of rotating machinery, and more specifically, to a method for identifying the axis track of a rotating machinery based on deep learning. Background technique [0002] Rotating machinery is an important part of mechanical equipment, and its motion state directly affects the working conditions of the entire equipment. Rotating machinery has the characteristics of high complexity, randomness and unpredictability of working conditions, and various failures will inevitably occur during the working process. Once forced to stop due to strong vibration, it will cause safety accidents such as stoppage of work and production, high maintenance and even machine crash, and even cause major social and economic impacts. Therefore, condition monitoring and fault diagnosis of rotating machinery are of great significance for evaluating equipment life, discovering potential faults, reducing maintena...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00G06N3/04
CPCG01M13/00G06N3/04
Inventor 杨嘉伟武利斌杨兵唐健田军
Owner 东方电气集团科学技术研究院有限公司
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