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Micro fault diagnosis method and device based on data driving

A data-driven, fault diagnosis technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as consuming diagnosis time, and achieve the effect of low parallelism and increased speed

Active Publication Date: 2021-02-09
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method requires a large amount of memory to store each step of information, and will consume a lot of diagnostic time due to sequential processing

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  • Micro fault diagnosis method and device based on data driving
  • Micro fault diagnosis method and device based on data driving
  • Micro fault diagnosis method and device based on data driving

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Embodiment Construction

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only It is a part of the embodiments of this application, not all of them. The components of the embodiments of the application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments of the present application, every other embodiment obtained by those skilled in the art withou...

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Abstract

The invention provides a micro fault diagnosis method and device based on data driving, and the method comprises the steps: preprocessing an obtained to-be-detected sample of a to-be-detected aircraft, obtaining a feature data set of the to-be-detected aircraft, inputting the feature data set into a pre-trained fault diagnosis model so as to obtain fault probabilities of multiple various fault types of the to-be-detected aircraft, wherein the fault diagnosis model comprises an input layer, a time convolution network layer and a Softmax classification layer; selecting the maximum fault probability selected from the fault probabilities of the different fault types, and determining the fault type corresponding to the maximum fault probability as the target fault type of the to-be-tested aircraft. Therefore, long-time information capture is realized through introduction of the time convolution network, and the method has the characteristics of parallelism and low memory, and is beneficialto improvement of the fault diagnosis speed of the hypersonic aircraft.

Description

technical field [0001] The present application relates to the technical field of fault detection, in particular, to a data-driven micro fault diagnosis method and device. Background technique [0002] Hypersonic vehicles generally refer to aircraft, missiles, artillery shells and other winged or wingless aircraft flying at speeds above Mach 5. In recent years, hypersonic aircraft has become the focus of research and competition among the world's military powers with its excellent flight speed and strong penetration capabilities. However, the hypersonic vehicle itself is a complex multivariable system, and it is also a dynamic closed-loop system. Small faults with small deviation and amplitude in the early stage of operation will slowly develop into significant faults in the later stage, and then the rapid diagnosis of small faults Research is urgently needed. [0003] In the prior art, a signal-based method and a data-based method are combined to implement fault diagnosis....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/044G06N3/045G06F18/214G06F18/2415G06F18/253
Inventor 宋佳艾绍洁尚维泽赵凯蔡国飙
Owner BEIHANG UNIV
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