Aero-engine gas circuit fault diagnosis method based on deep learning and information fusion

Abstract

The invention discloses an aero-engine gas circuit fault diagnosis method based on deep learning and information fusion, and belongs to the field of power machinery fault diagnosis. The method comprises the following steps: preprocessing data; secondly, respectively training a convolutional neural network model and a feedforward neural network to be optimal by utilizing the processed sample data,so as to obtain judgment results of the two models on the category to which the sample data belongs; thirdly, in the construction stage of the basic probability distribution of the evidence bodies, judging results of the two models are regarded as the two evidence bodies, in the stage, scores of all categories to which the samples in the evidence bodies belong are converted into the basic probability distribution of the samples, and meanwhile the uncertainty of the samples is calculated; and finally, in the evidence body synthesis and decision-making stage, calculating probability distributionof the two evidence bodies after synthesis according to a synthesis rule of a D-S evidence theory, and giving a category to which each sample belongs according to a decision-making rule. According tothe invention, decision results given by two deep neural networks after learning sample data from different angles are fused, an aerial fault diagnosis model with high precision is obtained, and theproblems of unstable discrimination capability and low robustness of a non-deep learning model for aerial big data from different sources can be overcome.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of power machinery, and relates to an aeroengine gas path fault diagnosis method based on deep learning and information fusion, which is used for real-time fault detection and fault classification of aeroengines or gas turbines. Background technique [0002] Aeroengines (hereinafter referred to as Aeroengines) are known as "the jewel in the crown of industry", and their internal system structure is extremely complex. Because of this, it is difficult for researchers to use the mechanical, fluid and thermodynamic expertise in the aerospace field to troubleshoot them. Most of the existing aviation fault diagnosis technologies are realized through the methods of big data and artificial intelligence. Fault detection and classification is a pattern recognition problem in the field of artificial intelligence. [0003] There are still some common problems in the practical application field of pattern reco...

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Problems solved by technology

[0003] There are still some common problems in the practical application field of pattern recognition such as aircraft fault diagnosis: the data format of aviation engine big data is not standardized

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