大跨越输电线路Beta阻尼线消振特性试验研究

针对传统极限学习机预测滚动轴承故障时，存在信号模式混叠、人为参数选取造成预测精度低下的问题，提出了正态分布-经验小波变换变换结合偏最小二乘法的极限学习机（partial least squares-extreme learning machines，简称PLS-ELM）的故障预测方法。首先，提出正态分布 经验小波变换信号降噪方法，通过正态分布划分频率带界限，在各频率带上构建带通滤波器进行降噪；其次，提出PLS-ELM的故障预测方法，应用偏最小二乘法（partial least squares，简称PLS）中主成分数和加载权重分别改进极限学习机(extreme learning machines，简称ELM）隐含层节点数和网络权值，激活函数选取Softmax以提高数据的拟合精度；最后，应用无量纲指标峭度来反映故障程度，实现故障趋势预测。试验结果表明，该方法能够准确划分频谱和克服模式混叠等问题，并实现滚动轴承性能衰退趋势预测。

Experimental Study on Damping Characteristics of Beta Damping Line for Large Span Transmission Lines

When the traditional extreme learning machine is used to predict the rolling bearing fault, there is a problem that the original signal pattern is aliased, and the artificial parameter selection causes the prediction accuracy to be low, and the fault prediction method of the normal distribution-empirical wavelet transformation combined with partial least squares based on the extreme learning machine method is proposed.Firstly,the normal distribution-empirical wavelet transformation signal de-noising method is proposed. The normal distribution is used to determine the interval number to divide the frequency band boundary. A band-pass filter is constructed and de-noised on each partition interval. Secondly, the fault prediction method of PLS-ELM is proposed, the principal component number and load weight of the partial least squares method are applied to improve the number of hidden layer nodes and the network weight of the extreme learning machine respectively. The activation function selects Soft max to improve the fitting accuracy of the data.Finally, the kurtosis of dimensionless index is used to reflect the fault degree and realize the fault trend prediction.The experimental results show that the method overcomes the problem of modal overlap and realize the prediction of performance deterioration trend of rolling bearing.