Fault diagnosis method and system driven by flight parameters of long-endurance unmanned aerial vehicle

By constructing a global reference time axis and a two-way time series estimation model, data compensation and state backtracking of flight parameters of long-endurance UAVs are performed, which solves the problem of data discontinuity during remote transmission and realizes the reliability and accuracy of fault diagnosis.

CN122087592AActive Publication Date: 2026-05-26NANJING TIANQING AEROSPACE TECH CO LTD
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Patent Information

Application Number
CN202610542715.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-23
Publication Date
2026-05-26
Estimated Expiration
2046-04-23

AI Technical Summary

Technical Problem

Flight parameters of long-endurance UAVs are prone to packet loss, out-of-order transmission, or discontinuity during long-distance transmission, making it difficult to fully trace the flight status. Traditional fault diagnosis methods are unable to identify gradual anomalies across long time scales, affecting the reliability and accuracy of fault analysis.

Method used

By constructing a global reference time axis, uniformly mapping multi-source flight data, using a two-way time series estimation model to dynamically predict and compensate for missing segments, combining a state space model to reconstruct flight state backtracking, and using a fault diagnosis model to perform multi-time scale feature analysis and fault cause analysis.

Benefits of technology

It achieves temporal consistency recovery of multi-source flight parameters, improves the reliability and interpretability of fault diagnosis for long-endurance UAVs, and accurately identifies the formation process and causes of flight faults.

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Abstract

The invention discloses a fault diagnosis method and system driven by flight parameters of a long-endurance unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: receiving multi-source flight data remotely transmitted by the unmanned aerial vehicle, and uniformly mapping the multi-source flight data to a global reference time axis for sampling continuity analysis, so as to recognize a missing section; for a continuous missing section, inputting a continuous flight parameter sequence before and after construction into a bidirectional time sequence estimation model, performing dynamic length prediction compensation to obtain a continuous flight parameter sequence, and performing flight state backtracking reconstruction based on a state space model to obtain a backtracking flight state sequence; extracting a multi-dimensional state feature vector from the backtracking flight state sequence, generating a fault diagnosis result by using a fault diagnosis model, constructing a fault evolution path and determining a fault cause by combining flight parameter abnormal trend analysis, parameter correlation analysis and environmental parameter auxiliary analysis, and realizing remote backtracking of the flight parameters of the long-endurance unmanned aerial vehicle. And the reliability of fault diagnosis is improved.
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