Fault feature extraction method of wind turbine gearbox based on ivmd-se

Abstract

The invention discloses a wind generation set gearbox fault characteristic extraction method based on IVMD-SE, and belongs to the technical field of the wind generation. The method comprises the following steps: respectively collecting original vibration signals of a wind generation set gearbox gear under a normal working status, an abrasion working status and a tooth breaking working status by utilizing a vibration acceleration sensor; optimizing a penalty term parameter a and a decomposition layer number K in the VMD parameter by adopting a PSO algorithm based on the integer planning; performing decomposition on the gear vibration signals under various working statuses by adopting an improved vibration modal method to obtain various modal components of the vibration signals; optimizingIMF components closely related to the original signals by utilizing the related coefficient method, and taking singular entropies of various modal components as wind turbine gearbox fault characteristics; inputting the characteristics into a multi-classification SVM to verify a characteristic extraction effect. The VMD and the singular entropies are combined to extract the wind generation set gearbox gear fault characteristics under the noise interference, the signal characteristics are strengthened, and the characteristic extraction effect is more obvious.

Description

Technical field [0001] The invention belongs to the technical field of wind power, and specifically relates to an IVMD-SE-based method for extracting fault features of a wind turbine gearbox. Background technique [0002] In recent years, with the continuous development of wind power generation technology, the installed capacity of wind turbines has become larger and larger, and the probability of its failure has increased year by year. The gearbox is a key component in the transmission system of a wind turbine. Once it fails, it will directly cause the shutdown of the entire unit and cause serious power loss. At the same time, the gearbox is also the component with the highest failure rate in the unit. Therefore, the timely fault diagnosis of the gearbox of the wind turbine is of great practical application significance for ensuring the safe and reliable operation of the wind turbine, ensuring the safety of property and related operators. [0003] Due to the complex operating co...

AI Technical Summary

Problems solved by technology

On the one hand, after the gearbox vibration signal is decomposed by VMD, there is still a certain amount of noise and irrelevant information in each IMF. On the other hand, because the fault information contained in each IMF is massive and irregular, it is difficult to directly analyze it. Purpose of fault identification

Due to these two reasons, it is difficult to effectively extract fault feature information.

[0006] At present, in the published patents and literatures, many scholars have conducted extensive research on how to effectively extract the fault features in the VMD modal components, and have achieved certain results, but these methods also have problems such as serious noise interference and computational complexity Disadvantages such as large size and low reliability

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