水电机组复杂工况振动信号多尺度清洗

GMM-DBSCAN multi-scale cleaning of vibration signals from hydropower units in complex operating conditions

水电机组振动监测信号常包含大量异常数据,严重影响机组健康状态评估与预测。为此,本文深入研究机组振动与工况的映射关系,提出了一种基于高斯混合模型和基于密度的噪点空间聚类算法(GMM-DBSCAN)的机组振动信号多尺度清洗方法。首先,采用DBSCAN初步清洗全工况内振动异常点,进一步采用高斯混合模型计算机组工况概率,并进行工况区间划分;在此基础上,以工况概率密度计算出各工况区内振动信号的DBSCAN密度聚类清洗的阈值,并清洗各工况区内振动异常数据。最后,基于瀑布沟水电站1年内运行工况和振动监测数据进行实例分析,异常数据清洗率达6.3‰。同时,通过人工模拟异常数据进行验证,结果表明,所提方法能够有效清洗出机组振动孤立离群点和密集异常点,为水电机组健康状态评估与预测奠定数据基础。

Most of the vibration monitoring signals of hydropower units contain a large amount of abnormal data,which severely affect the assessment and prediction of unit health status.Considering the relationship between the unit vibration and its working condition,this paper presents a multi-scale cleaning method of unit vibration signals based on Gaussian Mixture Model and Density Based Spatial Clustering Applications with Noise(GMM-DBSCAN).First,DBSCAN is used to clean initially the vibration anomalies in the whole range of working conditions,and GMM is used to calculate the probability of different working conditions and divide their intervals.Then,for each of the intervals,a density clustering cleaning threshold for DBSCAN is calculated using its probability density,and its abnormal vibration data are cleaned.This method has a cleaning rate of abnormal data of up to 6.3‰,which has been verified using the vibration data monitored at the Pubugou hydropower station under the different working conditions of its one-year operation.Meanwhile,artificial anomaly data are used to verify the method.The results show the method can effectively clean out the isolated outliers and dense abnormal points of unit vibration signals,thus improving the health status evaluation and prediction of hydropower units.