Intelligent aero-engine bearing fault migration diagnosis method based on multi-source data and attention mechanism

By dynamically optimizing the combination of modal features and capturing deep correlation characteristics through the attention mechanism, the problems of information redundancy and noise robustness in multimodal data fusion are solved, and efficient and intelligent diagnosis of aircraft engine bearing faults is achieved.

CN120654092APending Publication Date: 2025-09-16XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202510737480.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-04
Publication Date
2025-09-16

AI Technical Summary

Technical Problem

Existing multimodal data fusion methods fail to effectively mine modal association patterns, resulting in feature space redundancy or information loss, and lack robustness to noise and missing data, affecting diagnostic sensitivity and accuracy.

Method used

A multi-source data fusion method based on the attention mechanism is adopted to automatically learn the importance differences of different modalities under different fault conditions through attention weights, dynamically optimize the modal feature combination, capture the deep correlation characteristics between multimodal data, and suppress noise pollution.

Benefits of technology

It improves the robustness and diagnostic accuracy of multimodal data fusion, enhances the tolerance to noise, and realizes efficient and intelligent diagnosis of aircraft engine bearing faults.

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Abstract

The invention discloses an intelligent aero-engine bearing fault migration diagnosis method based on multi-source data and an attention mechanism, and the method comprises the steps: automatically learning the importance difference of different modes in different fault states through an attention weight, and achieving the dynamic optimization combination of mode features; meanwhile, an attention mechanism is utilized to capture deep correlation characteristics among multi-modal data of the aero-engine bearing part, space-time coupling relations among heterogeneous signals such as vibration, temperature and current are excavated, and an attention weight is utilized to automatically suppress modal contribution polluted by noise, so that noise robustness is enhanced; according to the method, the weight difference under different modes and different fault states can be automatically learned, the deep correlation of the modes is captured, the noise pollution is inhibited, and the robustness of the model is enhanced.
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