Design-Oriented product fault knowledge graph with frequency weight based on maintenance text

Li, Siqi; Wang, Junfeng; Rong, Jin

doi:10.1016/j.aei.2023.102229

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…There are currently two main types of research. One is based on knowledge graphs [1][2][3][4], involving multiple steps such as knowledge graph construction, knowledge learning, and knowledge reasoning. Key technologies include ontology modeling of domain knowledge, graph mining algorithms, and logical reasoning algorithms based on fast graph search.…”

Section: Research Backgroundmentioning

confidence: 99%

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Wu,

Guan

2024

Algorithms

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Under a distributed information system, the scale of various operational components such as applications, operating systems, databases, servers, and networks is immense, with intricate access relationships. The silo effect of each professional is prominent, and the linkage mechanism is insufficient, making it difficult to locate the infrastructure components that cause exceptions under a particular application. Current research only plays a role in local scenarios, and its accuracy and generalization are still very limited. This paper proposes a novel fault location method based on dynamic operation maps and alarm common point analysis. During the fault period, various alarm entities are associated with dynamic operation maps, and alarm common points are obtained based on graph search addressing methods, covering deployment relationship common points, connection common points (physical and logical), and access flow common points. This method, compared with knowledge graph approaches, eliminates the complex process of knowledge graph construction, making it more concise and efficient. Furthermore, in contrast to indicator correlation analysis methods, this approach supplements with configuration correlation information, resulting in more precise positioning. Through practical validation, its fault hit rate exceeds 82%, which is significantly better than the existing main methods.

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Section: Research Backgroundmentioning

confidence: 99%

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Wu,

Guan

2024

Algorithms

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Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Xiao,

Yan,

Zhang

et al. 2024

Auton. Intell. Syst.

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The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

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Combining improved DFMEA with knowledge graph for component risk analysis of complex products

Li,

Wang,

Rong

2024

Reliability Engineering & System Safety

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Design-Oriented product fault knowledge graph with frequency weight based on maintenance text

Cited by 5 publications

References 26 publications

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Fault Location Method Based on Dynamic Operation and Maintenance Map and Common Alarm Points Analysis

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Combining improved DFMEA with knowledge graph for component risk analysis of complex products

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