Triple concurrent fault analysis method, system, large unit equipment and storage medium

Abstract

The invention belongs to the technical field of gear fault analysis, and discloses a triple concurrent fault analysis method, system, large unit equipment and storage medium, which perform data acquisition and data preprocessing; perform friction fault feature extraction, including dimensionless and wavelet decomposition features; Use machine learning methods to build fault prediction models; make predictions on unknown label data. The present invention can effectively extract gear fault feature information, and provides a triple concurrent fault diagnosis method for oil film whirl, friction and rotor unbalance. Effective features can solve the problem of fault prediction well, and the dual-view method is used to make the features more comprehensive, and the feature dimension reduction is performed by the cca method, which makes the calculation process faster. The invention achieves good results on the problem of friction fault diagnosis of large units, and can concurrently and accurately predict three common mechanical faults.

Description

technical field [0001] The invention belongs to the technical field of gear fault analysis, and in particular relates to a triple concurrent fault analysis method, system, large unit equipment and storage medium. Background technique [0002] At present, the structure of large-scale equipment is complex, the functions are perfect, and the internal parts of the equipment are closely connected, which makes the production process high-speed and large-scale, which also makes the failure of large-scale equipment cause huge losses, which also increases It reduces the difficulty of fault diagnosis for mainframe equipment. [0003] The types of faults that often occur in large-scale equipment are oil film whirl faults, friction faults and rotor unbalance faults. These three account for the majority of mechanical faults. Therefore, how to diagnose mechanical equipment faults is among these three faults. The specific fault will play a great role in fault diagnosis and rapid repair of...

AI Technical Summary

Problems solved by technology

[0006] (1) The current common methods have difficulty in feature extraction and incomplete feature extraction

[0007] (2) The prediction accuracy of existing methods is not accurate enough

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