Airborne flight parameter data health diagnosis method based on deep learning

Abstract

The invention provides an airborne flight parameter data health diagnosis method based on deep learning. The method is based on a deep learning algorithm, data collected by aviation airborne flight parameter equipment can be analyzed in real time, the health condition of an airplane in the flight process is obtained, the fault risk of the airplane is predicted in advance, and a decision for avoiding faults is given to be adopted by pilots. According to the method, specific acquired data is required to be trained, and particularly, analysis and learning of relevance of various characteristic parameters before an airplane has a fault are the key to effectively predict and avoid the fault. And the problems of data real-time analysis, fault diagnosis and risk decision in the field of aviationairborne flight parameter equipment are effectively solved.

Description

technical field [0001] The invention belongs to the technical field of avionics, and in particular relates to a health diagnosis method for airborne flight parameter data. Background technique [0002] In recent years, due to the rapid development of artificial intelligence, deep learning has rapidly spread to all walks of life, such as face recognition, speech recognition and automotive driverless, but its application in the aerospace field is still relatively rare. In the process of traditional flight parameter data identification, in order to identify the characteristic parameters of the aircraft during flight, these characteristic parameters require multi-layer dimensions in the process of extraction. At this time, it is found that the model is difficult to train during the modeling process, and large-scale Parameter extraction will reduce the real-time performance of the system, and the aircraft needs to extract the characteristic parameters of various data in real time...

AI Technical Summary

Problems solved by technology

[0002] In recent years, due to the rapid development of artificial intelligence, deep learning has rapidly spread to all walks of life, such as face recognition, speech recognition and automotive driverless, but its application in the aerospace field is still relatively rare

In the process of traditional flight parameter data identification, in order to identify the characteristic parameters of the aircraft during flight, these characteristic parameters require multi-layer dimensions in the process of extraction. At this time, it is found that the model is difficult to train during the modeling process, and large-scale Parameter extraction will reduce the real-time performance of the system, and the aircraft needs to extract the characteristic parameters of various data in real time during the flight, and provide timely feedback of the characteristic parameters, so the traditional data recognition model can no longer reflect the characteristics of each parameter on the aircraft in time

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