Bearing fault diagnosis method and system for Meta-Transform driven multi-working-condition equipment

The Meta-Transformer-driven multi-condition bearing fault diagnosis method utilizes deep causal feature learning and meta-learning optimization frameworks to solve the problems of data scarcity and poor cross-condition adaptability in bearing fault diagnosis. It achieves high-precision and fast adaptive fault diagnosis and is suitable for CNC machine tool bearing condition monitoring in aerospace manufacturing.

CN121542830APending Publication Date: 2026-02-17DONGHUA UNIV
0 Cites 7 Cited by

Patent Information

Application Number
CN202511572999.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-30
Publication Date
2026-02-17

AI Technical Summary

Technical Problem

Existing bearing fault diagnosis methods in aerospace manufacturing suffer from problems such as data scarcity, poor adaptability across operating conditions, and neglect of physical causal constraints in the fault evolution process, resulting in low diagnostic accuracy and insufficient generalization ability under varying operating conditions.

Method used

A multi-condition equipment bearing fault diagnosis method driven by Meta-Transformer is proposed. Through deep causal feature learning and meta-learning optimization framework, combined with signal acquisition, causal Transformer encoder and multilayer perceptron classifier, it can achieve fast adaptive and high-precision fault diagnosis across operating conditions.

Benefits of technology

It significantly improves the accuracy of bearing fault diagnosis under small sample conditions, enhances the interpretability and generalization ability of the model, and solves the problem of decreased diagnostic performance caused by feature distribution shift and sample scarcity under multiple working conditions. It is particularly suitable for CNC machine tool bearing condition monitoring and fault prediction in the aerospace manufacturing field.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.
Need to check novelty before this filing date? Find Prior Art