Causal hierarchical network-based cross-condition bearing fault diagnosis method and system

Abstract

This invention belongs to the field of rotating machinery fault diagnosis technology, specifically disclosing a cross-condition bearing fault diagnosis method and system based on a causal hierarchical network. The method includes: acquiring bearing vibration signals and preprocessing them; for the preprocessed signals, Granger causality analysis and structural causality model learning are applied respectively to obtain Granger causality weight matrices and structural causality model weight matrices; based on the Granger causality weight matrices and structural causality model weight matrices, a causal sensing gating signal is generated using a gating mechanism; the causal sensing gating signal is used to enhance the features of the preprocessed signals; the feature-enhanced signal is input into a pre-trained causal hierarchical diagnosis network to obtain the fault diagnosis result. This invention integrates causal inference and deep learning, and effectively solves the problem of insufficient generalization ability caused by the reliance on statistical correlation in traditional methods by adaptively learning the causal representation of faults through a causal hierarchical network.

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