Motorcade load-crack joint evaluation method based on Bayesian update

By adopting a Bayesian-updated joint assessment method for fleet mission load and crack status, the problem of unreasonable inspection intervals caused by static load spectra was solved. This method enables dynamic assessment and real-time updating of aircraft structural loads and crack status, ensuring aircraft structural safety and maintenance efficiency.

CN122087947APending Publication Date: 2026-05-26BEIHANG UNIV
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Patent Information

Application Number
CN202610050964.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-15
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In the existing technology, the determination of aircraft structural inspection intervals relies on static load spectrum, which fails to fully consider the diversity of actual flight missions, resulting in inspection intervals that are too conservative or too aggressive, affecting the efficiency of maintenance resource allocation and structural safety.

Method used

A joint evaluation method for fleet mission load and crack state based on Bayesian update is adopted. By constructing a mission stochastic spectrum equivalent load model, Bayesian network and particle filtering method, dynamic evaluation and real-time update of aircraft structural load and crack state are realized. Combined with crack propagation model for joint modeling and diagnosis.

Benefits of technology

It enables accurate assessment of aircraft structural load levels and crack conditions without direct load measurement, supports condition-based structural maintenance, and ensures the integrity and safety of aircraft structures.

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Abstract

The invention provides a fleet mission load-crack state joint evaluation method based on Bayesian updating, and belongs to the field of aircraft structure safety, aircraft structure fatigue crack propagation tracking and aircraft load evaluation. Comprising the following steps: converting random load spectrums under different task processes of a fleet into equivalent constant-amplitude loads through an equivalent load method based on a Paris formula, and forming a load level capable of being quantitatively analyzed subsequently; establishing a task labeling load library and a fatigue crack damage expansion model, establishing a Bayesian network based on network nodes and interdependence relationships, and constructing a probabilistic task load-crack state joint evaluation architecture; adopting a particle filter algorithm as an inference algorithm of a dynamic Bayesian network, and performing joint evaluation on a fleet mission load-structure crack state; in combination with the network architecture and the method, testing and verification are carried out in a preset fleet. The load levels of different tasks of the fleet are evaluated in real time, so that the safety of the aircraft structure is guaranteed.
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