Fault early warning of power station equipment based on the multi-data fusion

With the increasing equipment automation degree of power plant and the increasing connection complexity within various equipments,the equipment fault risk under high temperature and high pressure is increased.In order to reduce the fault risk,the similarity principle is used to model the power plant equipment,and the relevant parameters are monitored and analyzed.The change of equipment parameter state can be detected in advance,so as to repair the specific equipment location to prevent the occurrence of serious faults.and achieve the purpose of early warning.For the comprehensive fault early warning of equipment,it is necessary to analyze the fault characteristic parameters from the real-time database of equipment.And the relevant maintenance records should be also considered.Consequently,it realizes the non-linear mapping from the characteristics,symptoms to the fault causes and fault types.When the deviation occurs between equipment parameters and predicted values,the alarm signal,which reminded of the relevant equipment operator for targeted inspection.Based on this method,the early warning system not only reduce the equipment fault occurrence,but also extend the equipment maintenance interval.In addition,it also has a broad application prospect in the field of large-scale equipment fault early warning.