Aircraft system fault diagnosis method based on MSCNN deep learning

A deep learning and aircraft system technology, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as incomplete expression of the diagnosed system, and achieve accurate results

Active Publication Date: 2019-01-04
SHANGHAI UNIV OF ENG SCI
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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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  • Aircraft system fault diagnosis method based on MSCNN deep learning
  • Aircraft system fault diagnosis method based on MSCNN deep learning
  • Aircraft system fault diagnosis method based on MSCNN deep learning

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Embodiment Construction

[0069] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0070] see figure 1 , figure 2 with image 3 , a kind of aircraft system fault diagnosis method based on MSCNN deep learning of the present invention, comprises the steps:

[0071] S1, collecting decoded aircraft QAR data;

[0072] The typical variables that can best reflect the working state of the aircraft system are selected as the input characteristic data of the model, and the threshold ranges of different working conditions are analyzed.

[0073] S2, transforming the aircraft state parameters in the aircraft QAR data into fixed-size two-dimensional data;

[0074] S3, establish the deep learning model MSCNN of the whole task profile;

[0075] S4, automatically identify the working conditions according to the samples and adaptively gener...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00
CPCG01M13/00
Inventor 周虹张兴媛陆文华
Owner SHANGHAI UNIV OF ENG SCI
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