Aircraft key part damage prediction method, system, equipment, medium and product

By generating sample dual-channel stress history and training a temporal convolutional network model, the problem of high time consumption in calculating aircraft damage values ​​in existing technologies is solved, achieving efficient damage prediction and meeting the needs of engineering applications.

CN122154223APending Publication Date: 2026-06-05BEIHANG UNIV
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Patent Information

Application Number
CN202610292131.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-11
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies consume a lot of time and computing power in calculating damage values ​​for aircraft structures, making it difficult to meet the engineering application requirements of life tracking for high-frequency updates.

Method used

By randomly generating multiple sample dual-channel stress histories, and combining material performance parameters and multi-axis fatigue calculation methods, a deep learning model based on a temporal convolutional network is trained, which is directly mapped to a data-driven fast prediction function to achieve end-to-end prediction of damage values ​​in critical parts of aircraft.

Benefits of technology

It significantly improves the computational efficiency of damage prediction for critical parts of aircraft, meets the needs of single-aircraft life tracking for high-frequency, low-latency damage assessment, and maintains high-precision damage value prediction capability.

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Abstract

The invention discloses an aircraft key part damage prediction method, system and device, a medium and a product, and relates to the field of aircraft life prediction, and the method comprises the steps: randomly generating a plurality of sample dual-channel stress courses for an aircraft key part; according to the material performance parameters, a multi-axial fatigue calculation method based on a critical plane method is adopted to calculate a sample damage value process corresponding to the dual-channel stress process of each sample; training a deep learning network model based on a time domain convolutional network by taking the sample dual-channel stress process as input and the sample damage value process as a label to obtain a fatigue damage prediction model; obtaining an actual measurement dual-channel stress process of the key part of the aircraft in the service process; and inputting the actually measured dual-channel stress history into the fatigue damage prediction model to obtain a corresponding damage value history, and realizing damage prediction of the key part of the aircraft. The method can improve the calculation efficiency of the damage value, thereby improving the prediction efficiency of the remaining life of the aircraft.
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