面向全量测点耦合结构分析与估计的工业过程监测方法OA北大核心CSTPCD

In the actual industrial scenario,it is necessary to collect a large number of data from measuring points in the production process,so as to master the operational state of the production process.Traditional process mon-itoring methods usually only evaluate whether the overall operation state is abnormal or not,or carry out hierarch-ical evaluation of the state.These methods do not directly locate the fault location,which is not conducive to the efficient maintenance of the fault.Therefore,in this paper,a monitoring model based on total measurement point estimation is proposed,and the monitoring indicators are defined according to the deviation between the estimated value and the actual value of total measurement points,so as to realize the separate and accurate monitoring of total measurement points.In order to overcome the problems of incomplete monitoring and insufficient modeling of coupling relationship between measuring points in the original monitoring method based on condition estimation,a multi-kernel graph convolution network(MKGCN)is proposed.By treating the measuring points as a graph of the total measurement points,the coupling relationship between measuring points is explicitly modeled,thus realizing the synchronous estimation of total measuring points.In addition,for the on-line monitoring scenario,a self-itera-tion method based on feature approximation is designed to overcome the issue of abnormal estimation of some measurement points due to the strong coupling between measurement points under abnormal system state.The method proposed in this paper is verified on the actual data of induced draft fan in 1 000 MW ultra-supercritical thermal power unit of power plant.The results show that the monitoring method proposed in this paper can detect the fault measuring points more accurately than other typical methods.