基于MCKD-EMD的风电机组轴承早期故障诊断方法

风电机组轴承处于早期故障阶段时,特征信号往往比较微弱,并且受环境噪声及信号衰减的影响严重,因此轴承早期故障特征一直难以提取。经验模态分解(EMD)在轴承的故障特征提取中已经得到了广泛的应用,但其在强背景噪声干扰下对轴承早期故障特征的提取具有一定的局限性。针对这一问题,考虑到最大相关峭度解卷积(MCKD)算法可凸显出轴承振动信号中被噪声所掩盖的故障冲击脉冲,非常适用于轴承早期故障信号的降噪处理,因此将MCKD与EMD相结合用于轴承早期故障诊断。用MCKD对强噪声轴承信号进行降噪,然后对降噪后的信号进行EMD,选取敏感本征模态函数(IMF)并计算其包络谱,通过分析包络谱中幅值凸出的频率成分判断故障类型。仿真和试验分析结果验证了所提方法的有效性和准确性。

Incipient bearing fault diagnosis based on MCKD-EMD for wind turbine

Since the characteristic signals of the incipient bearing fault of wind turbine are weak and seriously affected by the environmental noises and signal attenuation, it is difficult to extract them. Though EMD(Empirical Mode Decomposition) is widely used in the bearing fault feature extraction, it is not applicable to the feature extraction of incipient bearing fault when the background noise is strong. As MCKD(Maximum Correlated Kurtosis Deconvolution) algorithm can highlight the fault impact pulses of the bearing vibration signals masked by noises, it is combined with EMD to diagnose the incipient bearing fault. MCKD is applied to subdue the strong background noises and the de-noised signals are then treated by EMD to obtain the most sensitive IMFs(Intrinsic Mode Functions) and calculate the envelope spectrums, which is then analyzed to identify the frequency components with bigger amplitude for determining the fault type. The effectiveness and correctness of the proposed method are verified by simulation and experiment.