基于EWT-MDS的风力机轴承劣化趋势识别及故障诊断

WIND TURBINE BEARING DETERIORATION TREND IDENTIFICATION AND FAULT DIAGNOSIS BASED ON EWT-MDS

为解决风力机轴承劣化趋势识别精度低与早期故障诊断困难问题,提出一种基于经验小波分解和多维尺度变换的EWT-MDS方法。该方法首先将轴承全生命周期振动信号进行经验小波自适应分解,以信息熵为指标定量分析各模态分量的变化特点,然后结合多维尺度变换算法获取高维空间中各劣化表征分量的协同变化规律,与常规方法相比在检测效率和精度上有较大提升。仿真和实验结果表明该方法可提前检测轴承异常状态节点,通过轴承劣化表征模态分量时域重构,结合频谱和包络谱可准确判别轴承早期故障类型。

In order to solve the low identification accuracy of deterioration trend of wind turbine bearings and difficult problem of early fault diagnosis,a EWT-MDS method based on empirical wavelet decomposition and multidimensional scaling transform is proposed.Firstly,the method performs the adaptive empirical wavelet decomposition of the whole life cycle vibration signals of the bearings.The variation characteristics of various modal components are quantitatively analyzed by using information entropy as an index;then the multi-dimensional scaling transform algorithm is used to obtain the synergistic variation rule of each degradation component in high-dimensional space,which has a large improvement in detection efficiency and precision compared with the conventional method.The simulation and experimental results show that the proposed method can detect the nodal points of abnormal state of bearings in advance, and can accurately identify the early fault type of bearings through the time domain reconstruction of modal components characterized by bearing degradation and combining the spectrum and the envelope spectrum.