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A data-driven micro-fault diagnosis method and device

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: 2021-04-20
BEIHANG UNIV
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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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  • A data-driven micro-fault diagnosis method and device
  • A data-driven micro-fault diagnosis method and device
  • A data-driven micro-fault diagnosis method and device

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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 present application provides a data-driven micro-fault diagnosis method and device, wherein the method includes: preprocessing the acquired test samples of the test aircraft to obtain the feature data set of the test aircraft, and the feature data The collection is input into the pre-trained fault diagnosis model to obtain the fault probability of various fault types of the aircraft to be tested. The fault diagnosis model includes an input layer, a time convolutional network layer and a Softmax classification layer. The maximum failure probability is selected in the failure probability, and the failure type corresponding to the maximum failure probability is determined as the target failure type of the aircraft under test. In this way, this application realizes the capture of long-term information through the introduction of time convolution network, which has the characteristics of parallelism and low memory, and helps to improve the speed of fault diagnosis of 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....

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Application Information

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Patent Type & Authority Patents(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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