风电轴承多源振动信号故障诊断的数据融合方法研究

The Fault Diagnosis Method of Data fusion for the Multi-Source Vibration Signal of Wind Power Bearing

针对风电回转支承的故障振动信号为低频信号,不平稳、非线性,难以检出等难题,提出了基于数据融合的方案:首先提出了B型关联度加权法和小波去噪相结合的方法,对多源振动信号进行融合去噪,然后以EEMD为基础对融合后的信号进行分解,此方案不仅提高了融合信号的信噪比,而且抑制了在经验模态分解中的模式混叠现象,实验结果表明与理论计算的故障特征频率相吻合,充分显示了其应用在回转支承故障诊断系统中的可行性。

Aiming at the disadvantage of wind rotary bearing fault vibration signal,such as low frequency,not smooth,nonlinear,difficult to detect,it puts forward a project based on data fusion.First of all,the combination of methods correlation B weighting method and the wavelet de-noising is proposed,multi-source vibration signals are fused and de-noising and then are decomposed on the basis of the EEMD.The experimental results show that the project tallies with the theoretical calculation of the fault characteristic frequency,it fully shows the effectiveness of the system on the fault diagnosis of magnetic rotor.