Inplausible aircraft state sensing and predicting system and method

By expanding the perception layer, embedding physical information, and using SHAP-optimized neural networks, the problem of insufficient accuracy in servo fault diagnosis for high-speed aircraft has been solved, enabling lightweight, real-time abnormal state perception and prediction, thus ensuring the safety and controllability of aircraft.

CN121997452APending Publication Date: 2026-05-08SHANGHAI JIAOTONG UNIV
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Patent Information

Application Number
CN202511984722.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-26
Publication Date
2026-05-08

AI Technical Summary

Technical Problem

In high-temperature, high-pressure, and high-speed environments, the servo motors of high-speed aircraft are prone to failure, leading to attitude instability. Existing technologies have insufficient diagnostic accuracy and heavy computational burden, making it difficult to meet the real-time requirements of missiles.

Method used

By employing an extended perception layer, physical information embedding, and SHAP interpretable optimized neural network, the system predicts rudder deflection data through feature extraction and learning, and combines aircraft parameters and abnormal states to achieve lightweight and interpretable abnormal state perception and prediction.

Benefits of technology

It achieves millisecond-level online diagnosis, accurately identifies the type and severity of anomalies, predicts the aircraft status in advance, provides highly reliable safety assurance, and reduces the computational burden.

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Abstract

The invention relates to the technical field of high-speed aircraft health management, and particularly discloses an interpretable aircraft state sensing and predicting system and method. The method comprises the following steps: constructing and training a lightweight anomaly diagnosis network with an expansion perception layer; aircraft dynamics, kinematics and fault mechanism formulas are used as soft constraints to be embedded into the future state estimation network; an SHAP interpretation framework is used for sorting the importance of the input features, and a network structure is optimized accordingly; and finally, accurate diagnosis of abnormal types and positions and rapid prediction of aircraft states at a plurality of moments in the future are realized. According to the method, by introducing three innovation points of an expansion perception layer, physical information embedding and feature importance sorting, the diagnosis accuracy and the model interpretability are remarkably improved, and the high-performance calculation requirement in a scene with limited calculation power is met.
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