Semantic representation of steam turbine vibration fault knowledge based on ontology

LI Xiaobo; ZU Haidong; JIAO Xiaofeng; CHEN Dongchao; WANG Ting

doi:10.19666/j.rlfd.202105091

English

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Semantic representation of steam turbine vibration fault knowledge based on ontology

Thermal Power Generation Vol. 50, Issue 10, Pages: 78-86(2021)

更新时间：2025-09-28

- Semantic representation of steam turbine vibration fault knowledge based on ontology
- 作者机构：
  
  1. 内蒙古电力科学研究院
  2. 东北电力大学能源与动力工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202105091
  CLC： TK268;TP391.1
- Published：2021
- 稿件说明：
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LI Xiaobo, ZU Haidong, JIAO Xiaofeng, et al. Semantic representation of steam turbine vibration fault knowledge based on ontology[J]. Thermal Power Generation, 2021, 50(10): 78-86.
DOI：

LI Xiaobo, ZU Haidong, JIAO Xiaofeng, et al. Semantic representation of steam turbine vibration fault knowledge based on ontology[J]. Thermal Power Generation, 2021, 50(10): 78-86. DOI： 10.19666/j.rlfd.202105091.

摘要

针对汽轮机组的振动故障知识关联关系复杂、重用性高的特点

基于本体理论提出了故障知识的语义性建模方法。首先

构建汽轮机组设备结构、边界工况、故障知识的本体语义网

保留更丰富的语义信息

提高了对知识的重用和管理效率;其次

讨论了基于本体公理的知识一致性检验

避免知识的冲突和冗余

并采用本体查询语言提高故障知识检索的灵活性与效率;再次

将本体故障知识转化、存储到Neo4j图数据库中

形成故障知识图谱

将知识可视化;最后

通过具体的机组故障知识建模案例

说明了所提出方法的特点。

Abstract

Aiming at the characteristics of complex relationship and high reusability of vibration fault knowledge of steam turbine unit

a semantic modeling method of fault knowledge is proposed based on ontology theory. Firstly

the ontology semantic web of turbine equipment structure

boundary condition and fault knowledge are constructed to retain more abundant semantic information and improve the reuse and management efficiency of knowledge.Secondly

the knowledge consistency checking based on ontology axiom is discussed to avoid knowledge conflict and redundancy

and ontology query language is used to improve the flexibility and efficiency of fault knowledge retrieval. Thirdly

the ontology fault knowledge is transformed and stored in Neo4 j graph database to form the fault knowledge map and visualize the knowledge. Finally

the characteristics of the proposed method are illustrated by a case study of fault knowledge modeling.

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