A method and system for locating faults in a wind turbine drivetrain

By fusing explicit mechanical connections and implicit data associations through a dual-flow graph neural network model, the problem of fine-grained localization under rigid topology construction and small sample conditions in wind turbine drive chain fault diagnosis is solved, achieving high-precision, adaptive fault localization and continuous learning.

CN121808524BActive Publication Date: 2026-05-26NANJING UNIV OF INFORMATION SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING UNIV OF INFORMATION SCI & TECH
Filing Date
2026-03-10
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing wind turbine drivetrain fault diagnosis methods struggle to achieve precise localization under complex coupled operating conditions. In particular, they fail to balance physical mechanisms and implicit correlations under small sample conditions, and suffer from rigid topology construction, insufficient physical interpretability, and catastrophic forgetting in incremental learning.

Method used

A dual-flow graph neural network model is adopted, combining physical flow and latent flow. By introducing regularization constraints based on physical adjacency and an incremental learning mechanism, a fault location method for wind turbine drive chain is constructed. This method integrates explicit mechanical connections with implicit data associations to achieve high-precision fault location and continuous self-adaptation.

Benefits of technology

High-precision fault location of wind turbine drivetrain was achieved under small sample conditions, maintaining physical consistency and continuity of fault location results, and improving applicability and anti-disturbance performance in unstable industrial sites.

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Abstract

This invention discloses a method and system for locating faults in a wind turbine drivetrain. The method includes: constructing a dual-flow graph neural network model containing two paths: physical flow and latent flow. The input to the model is the features of each node constructed based on multi-channel vibration signals, and the output is an enhanced node representation. The physical flow and latent flow are used to extract explicit and implicit structural correlation features between nodes, respectively. During the training phase, physical consistency constraints are introduced to suppress unreasonable spatial jumps in the implicit correlation structure. During the model operation phase, a historical sample management mechanism based on sample information is combined to enable the model to continuously adapt to new fault categories without forgetting existing knowledge. Finally, the contribution of each sensor node is quantified based on the change in the original discriminant response to generate a fault location result consistent with the drivetrain topology. This invention can achieve refined and interpretable fault location in wind turbine drivetrain scenarios with small sample sizes and incremental learning.
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Citation Information

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