| In recent years, air pollution, especially the pollution caused by the burning of coal, is a serious threat to people’s lives. Whether living in the North or the South, people are able to share their feelings about air pollution. The rapid rise of the wind power industry, which is popular owing to its operation without the emission of carbon dioxide or other polluting gases, has injected new vitality into the development of new energy. It has discovered that the probability of turbine failure is increasing with the increase of wind turbines. How to find faults and propose solutions before the turbine completely collapsed has become very important. Gearbox is a key part of large-scale wind turbines, and it determines the performance of the whole wind turbine. Therefore, in this paper, we take the wind turbine gearbox as the research object.Vibration signals of the wind turbine gearbox consists of rich fault feature information which existed in the form of modulation. However vibration signals may contain plenty of random impulse noise, white noise, etc. Although wavelet de-noising has the advantage to depress the white noise, it cannot effectively suppress the impulse noise. The mathematical morphological filter has a strong ability to suppress impulse interference, but its ability to filter out white noise is weaker than the wavelet algorithm. The traditional time domain smoothing and frequency domain de-noising methods have many shortcomings. We use Lab VIEW as the development environment to analyze the vibration signal of gearbox. The integrated wavelet-mathematical morphology is used to achieve de-noising. According to the vibration mechanism and vibration model of gearbox, we use spectrum analysis, cepstrum analysis, envelope analysis and HHT signal vibration analysis method for vibration simulation signal and real signal is validated, Experiments presented in this paper show that thismethod can effectively diagnose the fault information of the gear box. |
PDF Full Text Request