基于摆度大数据的水轮发电机故障预测方法研究

摘要

水电厂积累的海量生产数据对发电机故障预测具有重要价值.本文采用蒙特卡洛方法对摆度大数据分析处理以进行故障预测.选取了某水电厂水轮发电机下导轴承摆度X峰峰值作为研究对象,在处理453 601组摆度数据时,选取3种典型工况进行研究.引入归一化加权平均值作为统计量,使用蒙特卡洛方法进行分析,发现该统计量服从正态分布.通过监测该统计量的实时值,依据正态分布3σ准则对水轮发电机工作状态做出定性评价,依据计算出的概率P值对水轮发电机工作状态定量评价,利用这些评价可以进行故障预测.%The hydropower plants have accumulated mass production data,which is of great value to failure prediction.In this paper,the Monte Carlo method is applied to analyze the big data of run-out value to predict the failure.The X peak-peak values of the run-out of the lower guide bearing of a hydropower plant are taken as the investigation object.In the treatment of 453,601 sets of the run-out value,three typical operating conditions are selected to study.The normalized weighted average is used as the statistic,and the Monte Carlo method is adopted to analyze it.It is found that the statistic obeys normal distribution.By monitoring the real-time value of the statistic,the working status of the hydro-generator is evaluated qualitatively according to the normal distribution 3σ criterion,and evaluated quantitatively according to the calculated probability P-value.Failure can be predicted by using these evaluations.