A Model-Based Method for Estimating Faults in Atmospheric Data

By employing a model-based fault estimation method and utilizing dual-model filters and Kalman filtering techniques, the problem of fault identification and isolation in atmospheric data systems was solved, achieving accurate fault detection and estimation, and improving system availability and aircraft safety.

CN116256005BActive Publication Date: 2026-03-06NANJING UNIV OF AERONAUTICS & ASTRONAUTICS +1
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Patent Information

Application Number
CN202310289718.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-23
Publication Date
2026-03-06
Estimated Expiration
2043-03-23

AI Technical Summary

Technical Problem

Existing technologies struggle to identify fault types and characteristics in atmospheric data systems, especially in common-mode faults where it is difficult to properly isolate faults, leading to inaccurate flight parameter outputs and impacting aircraft safety.

Method used

A model-based fault estimation method is adopted. By constructing a dual-model filter, using unscented Kalman filtering for filter updates, calculating model probabilities, and identifying fault information sources through residual component chi-square detection, the filter is selectively initialized to achieve accurate estimation of airspeed, angle of attack, and sideslip angle.

Benefits of technology

It effectively reduces hardware redundancy requirements, enables timely detection and identification of fault information sources, and improves the availability of the atmospheric data system and the safety of the aircraft.

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Abstract

This invention discloses a model-based method for atmospheric data fault estimation, comprising the following steps: acquiring measurement information; constructing the state equation and measurement equation of a dual-model filter; updating the dual-model filter using an unscented Kalman filter; calculating the model probability of the dual-model filter and determining whether an atmospheric data system fault has occurred based on the model probability; identifying the fault information source using the residual component chi-square detection method when a fault occurs, and selectively reinitializing the dual-model filter; calculating and outputting atmospheric data estimates and atmospheric data fault estimates based on the state estimation results of the dual-model filter. This method can promptly detect bias faults in the atmospheric data system and ensure accurate estimation of airspeed, angle of attack, and sideslip angle under fault conditions, further improving the reliability of the atmospheric data system and playing a significant role in ensuring safe flight of aircraft.
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