文献类型：期刊文献

中文题名：基于实时动态基线的运行设备多元状态估计方法

英文题名：Multivariate state estimation technique for equipment running condition using real-time dynamic baseline

作者：胡杰[1];唐静[2,3];谢仕义[1]

机构：[1]广东海洋大学数学与计算机学院,广东湛江524088;[2]北京石油化工学院信息工程学院,北京102617;[3]远光软件股份有限公司,广东珠海519085

年份：2021

卷号：50

期号：2

起止页码：125

中文期刊名：热力发电

外文期刊名：Thermal Power Generation

收录：CSTPCD、、CSCD2021_2022、北大核心、CSCD、北大核心2020

基金：广东省教育厅创新强校项目(Q18286)。

语种：中文

中文关键词：设备状态监测;故障诊断;大数据;多元状态估计;动态基线

外文关键词：equipment status monitoring;fault diagnosis;big data;multivariate state estimation;dynamic baseline

中文摘要：针对稳定运行的设备状态监测问题,本文提出基于实时动态基线的运行设备多元状态估计方法。该方法通过对运行设备监测所得大数据的学习和训练,构造稳定运行工况下的设备健康矩阵;然后针对设备当前运行观测值,计算实时偏差;根据偏差构建实时动态基线,实现对设备运行状态的判断,通过及时反馈故障成因的贡献率定位异常测点。将该方法应用于某机组风烟系统,结果表明:该方法能在早期准确发现设备异常状态并进行故障诊断,有效解决设备异常状态固定阈值判断的不确定性和滞后性,降低设备故障误判概率。

外文摘要：Aiming at the problem of equipment monitoring in stable operation, a method that based on dynamic baseline in real time is proposed for equipment multiple state estimation. In this method, an equipment health matrix is constructed under stable operating conditions through associative learning and training of equipment running in big data. Then, the real-time deviation of the equipment is calculated according to the current operating observation data, and a real-time dynamic baseline is constructed based on the deviation to realize the judgment for the equipment operating status, and then the abnormal measuring points will be located by timely feedback of the contribution rate of the cause of the failure. This method is applied on air and flow gas system of a unit. The test results show that, this dynamic baseline method can detect the equipment anomalies as early as possible and then carry out fault diagnosis, which effectively solves the uncertainty and lag of fixed thresholds, reduces the probability of misdiagnosis of equipment fault. This method can accurately detect the abnormal state of the equipment and diagnose the fault in the early stage, effectively solve the uncertainty and lag of the fixed threshold judgment of the abnormal state of the equipment, and reduce the probability of fault misjudgment of the equipment.