An aircraft system fault diagnosis method based on mscnn deep learning

Abstract

The invention discloses an aircraft system fault diagnosis method based on MSCNN deep learning. The method comprises the following steps of S1, collecting decoded aircraft QAR data; S2, converting anaircraft state parameter in the aircraft QAR data into fixed-size two-dimensional data; S3, establishing the deep learning model MSCNN of a full mission profile; S4, according to an automatic sample identification working condition, adaptively generating a single working condition model, using the deep learning model MSCNN to automatically detect sample data which needs to be detected, and identifying a fault under a single working condition; and S5, comparing the diagnostic results of multiple working conditions, carrying out redundancy and verification, and acquiring a final diagnosis result.

Description

technical field [0001] The invention belongs to the technical field of aviation system fault diagnosis, and specifically relates to an aircraft system fault diagnosis method based on MSCNN deep learning. Background technique [0002] With the current aircraft system equipment becoming increasingly complex, accurate and effective fault diagnosis of complex equipment systems through intelligence and mechatronics has become an effective way to improve system safety and reliability and reduce maintenance costs. The current methods mainly include case-based method, expert system, fuzzy reasoning method, etc. These methods rely too much on the diagnosis experience of engineers and experts, and some fault phenomena are difficult to reproduce, so it is difficult to meet the fault diagnosis requirements of modern complex system equipment. [0003] Aircraft QAR data is accompanied by massive monitoring data, and deep learning learns, interprets and analyzes learning input data by esta...

AI Technical Summary

Problems solved by technology

[0004] However, for the traditional CNN model, only one Softmax classifier is included, and for multi-working conditions, one CNN model cannot fully express the diagnosed system

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