Wind turbine gear box fault recognition method

Abstract

The invention discloses a wind turbine gear box fault recognition method. The method comprises the following steps: historical wind turbine gear box operation data in a certain time range are acquired; autocorrelation analysis is adopted for carrying out wavelet de-noising processing on the historical data; through fast Fourier transform, time domain and frequency domain characteristic parameters in the historical data after de-noising are extracted; a kernel principal component analysis method is adopted to carry out dimensionality reduction on the characteristic parameters, and several nonlinear principal elements with the maximum variance cumulative contribution rate are extracted; the nonlinear principal elements extracted by the historical gear box normal operation data are used for building a normal model, a support vector machine is used for training to guide the nonlinear principal elements extracted by later gear box operation historical data to the model after training, and thus, the gear box fault is recognized. The vibration signal processing ability is improved, and an important role is played in gear box fault recognition.

Description

technical field [0001] The invention relates to the field of fault monitoring and fault diagnosis of a transmission system of a wind turbine, in particular to a fault identification method for a gearbox of a wind turbine based on fast Fourier transform, kernel principal component analysis and support vector machine. Background technique [0002] With the rapid development of wind energy, a large number of wind turbines have been put into operation, and because most of the wind turbines are installed in remote areas, the load is unstable and other factors, many wind turbines in my country have malfunctioned, which will directly affect the safety of wind power generation sex and economy. In order to make wind power generation more competitive and ensure continuous and efficient operation of wind turbines, the importance of maintenance services such as condition monitoring, fault diagnosis, and repair of wind turbines has attracted widespread attention. Among them, the damage t...

AI Technical Summary

Problems solved by technology

The commonly used time domain and frequency domain feature information extraction methods often contain a lot of redundant information, so that the accuracy of the signal is not high, it is difficult to accurately evaluate and reveal the inherent characteristics of the wind turbine operating state, and cannot effectively reflect the current state of the equipment

The wavelet analysis technology based on the time-frequency domain can meet the above requirements, but in practical applications, the extracted equipment signals often have a strong noise background. How to further process these fault signals is a major obstacle in signal analysis.

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