公开(公告)号：US20240403599A1

公开(公告)日：2024-12-05

申请号：US18475200

申请日：2023-09-26

Applicant: ZHEJIANG UNIVERSITY OF TECHNOLOGY

Inventor： Gang Xiao ,  Jiacheng Huang ,  Yuanming Zhang ,  Zhenbo Cheng ,  Xuesong Xu ,  Jiawei Lu ,  Qibing Wang

IPC: G06N3/042 ,  G06N3/048 ,  G06N3/08

Abstract: Disclosed in the present invention is a health state assessment method for equipment based on a knowledge graph attention network, includes: steps: 1) constructing a graph data model which can comprehensively reflect change of a health state of the equipment by deeply integrating association relationships of equipment components, monitoring data dependence relationships and priori information, etc. by means of a knowledge graph and by combining with domain priori knowledge; 2) extracting feature information of the health state knowledge graph by using a graph attention network, and obtaining a target node vector representation which accurately reflects the health state of the equipment by means of learning; and 3) making a health state representation vector of the equipment pass through a fully connected layer to obtain a health state classification prediction probability, and performing training to reducing a loss value relative to a true label, thereby obtaining a health state assessment result.