Health Status Assessment on the Operation Function of Nuclear Power Plants Based on Multi-level Data Fusion
With the new-generation information technologies and artificial intelligence that are deeply integrated with the industry,the maintenance of industrial systems is moving from the manual regular maintenance to state-based intelligent maintenance(IM).Health status assessment(HSA)is a key link in IM.Systemic states are recognized via the monitored data whereby provide decision support for maintenance.Taking nuclear power plants(NPP)as the research object,an HSA framework is proposed based on multi-level integration of equipment,system,sub-function and function in a multi-department collaboration.Due to equipment groups with numerous state parameters,a weighted average fusion operator based on deviation weighting is proposed to fuse equipment-level parameters,which can timely highlight the abnormal equipment.Considering the different amount of data in different health states,an asymmetric multi-class learning method under imbalanced datasets is proposed to fuse the systems'health values.The self-learning HSA model is established by the information transfer between multiple health levels,so that the health status of sub-functions can be self-perceived and assessed in a timely manner.The health assessors of multiple sub-functions are fused based on ensemble learning to obtain a macro-level operational function HSA decision.Exemplified with the reactivity function of the NPP,the proposed HAS frame is verified effective.
industrial systemhealth status assessmentdata fusionnuclear power plantstate-based maintenance
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