Abstract The mechanism analysis of the sudden change in load inertia caused by the failure of large petrochemical fan blades is unclear, which makes it difficult to diagnose imbalanced samples faults based on data-driven methods and has the problem of poor interpretability. Based on this issue, a fault diagnosis method for imbalanced samples of broken blades in large petrochemical fan under sudden changes in load inertia is proposed. This method firstly establishes a failure mechanism model for large petrochemical fan blades, revealing the physical characteristics between inertia, torque and speed under sudden changes in load inertia. Secondly, based on the failure mechanism model, the fault characteristics of broken blades in petrochemical fan are extracted to solve the problem of difficult feature extraction of fault samples in imbalanced samples. Finally, a data-driven diagnostic model was constructed under the constraint of sudden changes in load inertia to improve the interpretability of fault diagnosis for large petrochemical fan with broken blades. The experiment shows that the proposed method significantly improves the diagnostic accuracy and effectiveness for detecting faults in broken blades of petrochemical fans, achieving a fault diagnosis accuracy of 99.03%.
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