基于知识图谱的燃气轮机故障诊断知识库构建方法及维护

燃气轮机机组的设备健康状况的改变容易受到多种因素的影响，并且各原因间存在着复杂的耦合与传递关系，以往传统的故障诊断系统常会出现一种故障征兆对应多种故障模式的问题，针对此问题，目前主流解决方案已有基于知识图谱的故障诊断方法，但针对燃气轮机故障知识库的构建尤其是知识更新的研究较少，因此本文将重点从基于知识图谱的燃气轮机故障诊断中的知识库构建及维护进行进行介绍。首先，阐述了本体基本理论以及基于本体的语义推理原理，其次通过阐述领域知识的获取及表示介绍了知识库的构建过程，避免了在新知识整合过程中出现冲突及冗杂情况，最后通过实例验证了基于知识图谱的燃气轮机故障诊断知识库构建方法的可行性，及知识更新的稳定性，有效的解决了常规智能诊断过程中推理“一对多”的问题，同时提供了一种较为准确且稳定的知识图谱更新方法，为其他机械设备的故障检测提供了一条新思路。

Method And Maintenance Of Building A Gas Turbine Fault Diagnosis Knowledge Base Based On Knowledge Graph

The equipment health status of gas turbine units is easily influenced by various factors, and there exist complex coupling and transmission relationships among these factors. Traditional fault diagnosis systems often face the problem of one symptom corresponding to multiple fault modes. To address this issue, the mainstream solution currently is the fault diagnosis method based on knowledge graph. However, there is limited research on the construction and knowledge updating of the gas turbine fault knowledge base, which is why this article focuses on introducing the knowledge base construction and maintenance of gas turbine fault diagnosis based on knowledge graph. Firstly, the basic theory of ontology and the semantic reasoning principle based on ontology are explained. Secondly, the process of knowledge base construction is introduced through the acquisition and representation of domain knowledge, which avoids conflicts and redundancies in the integration of new knowledge. Finally, the feasibility of the method is verified through examples, and the stability of knowledge updating is ensured. This effectively solves the problem of "one-to-many" reasoning in conventional intelligent diagnosis, and provides a more accurate and stable knowledge graph updating method, which offers a new approach for fault detection of other mechanical equipment.