Aero-engine transmission system fault diagnosis method based on domain adaptive graph convolutional network

A convolutional network and domain self-adaptive technology, applied in neural learning methods, biological neural network models, testing of mechanical components, etc., can solve problems such as ignoring data structure modeling, incomplete information, etc., to improve distinguishability, Reduce the effect of domain differences

Active Publication Date: 2021-08-27
XI AN JIAOTONG UNIV
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  • Description
  • Claims
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Problems solved by technology

However, most of the existing unsupervised domain adaptation methods only use the first two kinds of information and ignore the modeling of the data structure, which makes the information contained in the features extracted by the deep network incomplete.

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  • Aero-engine transmission system fault diagnosis method based on domain adaptive graph convolutional network
  • Aero-engine transmission system fault diagnosis method based on domain adaptive graph convolutional network
  • Aero-engine transmission system fault diagnosis method based on domain adaptive graph convolutional network

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[0063] The following will refer to the accompanying drawings Figure 1 to Figure 6 Specific embodiments of the present disclosure are described in detail. Although specific embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art should understand that they may use different terms to refer to the same component. The specification and claims do not use differences in nouns as a way of distinguishing components, but use differences in functions of components as a criterion for distinguishing. "Include...

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Abstract

The invention discloses an aero-engine transmission system fault diagnosis method based on a domain adaptive graph convolutional network. The method comprises the steps of: collecting first vibration signals of an aero-engine transmission system with an unknown fault at different rotating speeds, and taking the first vibration signals as unlabeled target domain data; constructing a domain adaptive graph convolutional network, collecting second vibration signals of an aero-engine transmission system with a known fault at different rotating speeds, taking the second vibration signals as labeled source domain data, and taking the source domain data and part of the target domain data as input to train the domain adaptive graph convolutional network; and inputting the other part of target domain data into the trained domain adaptive graph convolutional network to obtain a prediction label of the target domain data. Migration diagnosis of a fault of an aero-engine transmission system at different rotating speeds is realized.

Description

technical field [0001] The disclosure belongs to the field of mechanical fault diagnosis, and in particular relates to a fault diagnosis method for an aircraft transmission system based on a domain-adaptive graph convolution network. Background technique [0002] Intelligent diagnosis plays an important role in the health management system of aero-engine transmission system, which has been widely used in modern industry, and its main purpose is to monitor the status of equipment and reduce downtime. At present, unsupervised domain adaptive methods have been successfully applied in mechanical fault diagnosis under variable working conditions. In unsupervised domain adaptation methods, three types of information, such as class label, domain label and data structure, are crucial to achieve the process of transferring from a labeled source domain to an unlabeled target domain. However, most existing unsupervised domain adaptation methods only use the first two kinds of informat...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/028G06K9/62G06N3/04G06N3/08
CPCG01M13/028G06N3/08G06N3/045G06F18/24Y02T90/00
Inventor 孙闯李天福赵志斌王诗彬田绍华严如强陈雪峰
Owner XI AN JIAOTONG UNIV
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