面向飞机电源系统故障诊断的知识图谱构建技术及应用

聂同攀; 曾继炎; 程玉杰; 马梁

doi:10.7527/S1000-6893.2021.25499

航空学报 >

2022 , Vol. 43 >Issue 8: 625499 - 625499

DOI: https://doi.org/10.7527/S1000-6893.2021.25499

故障诊断技术在航空航天领域中的应用专栏

面向飞机电源系统故障诊断的知识图谱构建技术及应用

聂同攀 ,
曾继炎 ,
程玉杰 ,
马梁

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1. 北京航空航天大学可靠性工程研究所, 北京 100083;
2. 航空工业第一飞机设计研究院, 西安 710089;
3. 可靠性与环境工程技术国防科技重点实验室, 北京 100083;
4. 北京航空航天大学可靠性与系统工程学院, 北京 100083

收稿日期: 2021-03-16

修回日期: 2021-08-26

网络出版日期: 2021-08-25

基金资助

国家自然科学基金(61803013,61973011,61903015);航空科学基金(ASFC-201933051001)

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Knowledge graph construction technology and its application in aircraft power system fault diagnosis

NIE Tongpan ,
ZENG Jiyan ,
CHENG Yujie ,
MA Liang

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1. Institute of Reliability Engineering, Beihang University, Beijing 100083, China;
2. AVIC the First Aircraft Insititute, Xi'an 710089, China;
3. Science & Technology on Reliability & Environmental Engineering Laboratory, Beijing 100083, China;
4. School of Reliability and System Engineering, Beihang University, Beijing 100083, China

Received date: 2021-03-16

Revised date: 2021-08-26

Online published: 2021-08-25

Supported by

National Natural Science Foundation of China (61803013,61973011,61903015);Aeronautical Science Foundation of China (ASFC-201933051001)

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摘要

随科技水平发展,飞机机载设备的电气化程度越来越高,飞机电源系统故障对飞机飞行安全的威胁也不断增大,因此需快速准确地判断其健康状态。常用的基于数据驱动的故障诊断方法无法利用专家知识,且结果可解释性差,为实际使用带来了不便。知识图谱具备将专家知识等非结构化数据进行规范化存储并用于故障诊断的能力,能实现对非结构化先验知识的利用及故障原因的解释。然而在故障诊断领域,知识图谱技术的应用尚不多见。因此本文提出了一种面向飞机电源系统故障诊断的知识图谱构建及应用技术。首先,利用专家知识构建知识图谱的本体,明确知识图谱中的实体和关系类型;然后,使用BMEO标注的实体抽取语料训练双向长短期记忆网络(LSTM)并利用其从非结构化文本中抽取实体;在此基础上,使用关系标注后的关系抽取预料训练基于注意力机制的双向长短期记忆网络,进而利用训练好的模型进行实体间的关系抽取;最终利用抽取出的实体和关系构建面向飞机电源系统故障诊断的知识图谱。本文以飞机电源系统故障排故手册为原始数据,对提出的知识图谱构建方法进行了案例验证,以准确度和召回率等指标分析了所用方法的实体抽取和关系抽取效果,证明了其有效性。在此基础上,本文基于构建的知识图谱实现了飞机电源系统相关故障的智慧搜索、推荐及智能问答,展示了知识图谱技术在故障诊断领域具备的良好应用前景。

关键词： 知识图谱; 故障诊断; 飞机; 电源系统; 双向长短期记忆网络

本文引用格式

聂同攀 , 曾继炎 , 程玉杰 , 马梁 . 面向飞机电源系统故障诊断的知识图谱构建技术及应用[J]. 航空学报, 2022 , 43(8) : 625499 -625499 . DOI: 10.7527/S1000-6893.2021.25499

Abstract

The electrification degree of airborne equipment continues to increase with the development of science and technology. Owning to this, failure in aircraft power system is posing an increasing threat to flight safety, which granted necessity to fast and accurate health state assessment. The commonly used data-driven fault diagnosis method cannot make use of expert knowledge. Meanwhile, the result of data-driven method is lack of interpretability, and therefore, limits its application in real practice. Knowledge graph has the ability to normalize the storage of such unstructured data as expert knowledge and use it for diagnosis. Moreover, knowledge graph can utilize the unstructured knowledge and supply a reasonable explanation for the cause of the failure. However, in the field of fault diagnosis, there are still few studies on the application of knowledge graph technology. In this article, a knowledge graph construction and application technology for aircraft power system fault diagnosis is proposed. First, ontology of the knowledge graph, which specifies the entity and relation types in the knowledge graph, is constructed based on the priori expert knowledge. Then, Bi-Directional Long Short-Term Memory (Bi-LSTM) method is trained with BMEO-tagged corpus and utilized to extract entities from the unstructured texts. After that, an attention-based Bi-LSTM algorithm is trained with relation-tagged corpus and then utilized to realize relation extraction. Finally, the knowledge graph for aircraft power system fault diagnosis is constructed based on the extracted entities and relations. A fault isolation manual of aircraft power system is used as raw corpus data in the case study to verify the effectiveness of the proposed method by the indicators of precision and recall. Based on the knowledge graph, intelligent searching, recommending and Q & A are realized, which strongly support the application prospect of knowledge graph in the field of fault diagnosis.

Key words： knowledge graph; fault diagnosis; aircraft; power supply system; long short-term memory

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