水轮发电机组非平稳振动信号的检测与故障诊断

针对传统方法难以精确检测水轮发电机组的非平稳振动信号以及现有振动故障诊断方法精度低等问题,本文首先引入排列熵算法对其进行检测与分析,进而引入多维度排列熵算法,以实现对非平稳振动信号的特征提取,构造故障样本数据,并将其作为基于遗传算法的支持向量机诊断模型的输入,从而完成故障的诊断与识别。仿真实例表明,排列熵能够有效检测非平稳振动信号的突变,多维度排列熵与支持向量机相结合的故障诊断方法可有效识别机组的异常情况,具有较高的诊断精度。

Detection for non-stationary vibration signaland fault diagnosis ofhydropower unit

In view of the traditional method is difficult to accurately detect non-stationary vibration signal of hydro-generator units and the low accuracy of existing vibration fault diagnosis methods,this paper introduced the permutation entropy algorithm for detection and analysis. And then realized feature extraction of non-stationary vibration signals based on multi-dimensional permutation entropy,so as to construct fault data samples; The diagnosis model of support vector machine (SVM) based on genetic algorithm is established, and the sample data is the input of the model, then the fault diagnosis and identification is completed. The simulation results show that permutation entropy can effectively detect the mutations of non-stationary vibration signals,and the fault diagnosis method based on MPE and SVM can effectively identify abnormal situation of the unit and achieve higher diagnostic accuracy.