Health state assessment method for equipment based on knowledge graph attention network

Patent metadata

Field	Value
Publication number	US-2024403599-A1
Application number	US-202318475200-A
Country	US
Kind code	A1
Filing date	Sep 26, 2023
Priority date	May 31, 2023
Publication date	Dec 5, 2024
Grant date	—

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.

First claim

What is claimed is:

1 . A health state assessment method for equipment based on a knowledge graph attention network, comprising:
step 1) constructing a health state knowledge graph of the equipment, comprising:
step 1.1) extracting component entities and relationships;
step 1.2) extracting monitoring index entities and relationships; and
step 1.3) constructing the knowledge graph;
step 2) performing representation learning of the knowledge graph based on the graph attention network, comprising:
step 2.1) achieving input and output of the graph attention network;
step 2.2) calculating an attention coefficient of a central node;
step 2.3) performing node feature aggregation based on a multi-head attention mechanism; and
step 2.4) achieving vector representation of the health state knowledge graph; and
step 3) performing health state assessment on the equipment based on representation learning.

2 . The health state assessment method for equipment based on the knowledge graph attention network according to claim 1 , wherein the step 1.1) comprises:
separately extracting the component entities and relationships according to a composition relationship between a system and components, wherein each non-divisible component represents a component entity, and the component relationships comprise an energy transfer relationship, a structure composition relationship and a control relationship; wherein the step 1.2) comprises: extracting the monitoring index entities and the monitoring relationships with the component entities from time series data, and performing normalization processing; performing division by using a time sliding window technique for each piece of monitored time series data {x t 1 , x t 2 , x t 3 , . . . , x t n }, setting a window size as b, and aggregating a monitored value set of each window, wherein a common aggregation method is to find an average value, that is, if x t y ∈[x t 1 ,x t 1 +b),

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normalizing different monitoring data by a calculation formula (1) being as follows:

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wherein x norm i represents a normalized value of the ith sensor, x i represents data collected by the ith sensor, and x mean i and ∂ i represent a mean value and a variance of an original measurement value of the ith sensor respectively; and
wherein the step 1.3) comprises:
constructing the health state knowledge graph of the equipment according to the extracted component entities, component relationships, monitoring index entities and monitoring relationships, wherein component nodes represent health states of components at a certain moment, and a formal definition is as follows:
the health state knowledge graph of the equipment is a directed graph which is composed of the component entities, the monitoring index entities, the component relationships and the monitoring relationships, has time labels, and is expressed as G=(E, R, T, τ), wherein E is an entity set, which comprises the component entities and the monitoring index entities, R is a relationship set with time stamps, which is used for representing factual relationships comprising the component relationships and the monitoring relationships, τ represents a current time stamp of the knowledge graph, T={(h, r, t)|h, t∈E, r∈R} is a set of triples, and
the constructed health state knowledge graph with the time labels is capable of expressing the relationship between a health state of the equipment at each moment and a monitoring index.

3 . The health state assessment method for equipment based on the knowledge graph attention network according to claim 1 , wherein the step 2) comprises:
embedding the health state knowledge graph into a unified vector representation space by using a graph attention network model to obtain vector representations of the entities and relationships, and then using a vector representation of a target entity for subsequent health state assessment of the equipment; wherein the step 2.1) comprises: defining a node feature of input of the graph attention network as h={h 1 , h 2 , . . . , h N }, h i ∈R F , wherein N is the number of nodes, F is a dimension of the node feature, an output new feature vector is F′ after passing through the graph attention network, and an output feature vector is represented as h={h 1 ′, h 2 ′, . . . , h N ′}, h i ′∈R F′ ; wherein the step 2.2) comprises: in order to obtain sufficient expression ability, converting an input feature into a higher-level vector representation, calculating attention coefficients between the central node and neighbor nodes thereof one by one by a calculation formula (2) being as follows:

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Assignees

• Univ Zhejiang Technology

Inventors

• Xiao Gang
• Huang Jiacheng
• Zhang Yuanming
• Cheng Zhenbo
• Xu Xuesong
• Lu Jiawei
• Wang Qibing

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/042 Primary

  Knowledge-based neural networks; Logical representations of neural networks · CPC title