Fault Knowledge Graph Construction and Platform Development for Aircraft PHM

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-12, Vol.24 (1), p.231
• Main Authors: Meng, Xiangzhen, Jing, Bo, Wang, Shenglong, Pan, Jinxin, Huang, Yifeng, Jiao, Xiaoxuan
• Format: Article
• Language: English
• Subjects: Aircraft; Algorithms; Construction; Control systems; Decision making; Deep learning; Efficiency; Failure; Fault diagnosis; Graphs; joint extraction of entity relationships; knowledge graph; Knowledge representation; PHM; Q&A system; Semantics; Trouble shooting
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24010231

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.