Enterprise AI Analysis
Research on construction technology and application of knowledge graph in equipment fault Diagnosis domain
In recent years, the construction technology of general knowledge graph (KG) has been developing continuously. The medical industry, manufacturing industry, financial industry and other industries have constructed domain KGs. The research of KG in the equipment field is mainly focused on general equipment knowledge, and the field of equipment fault diagnosis also needs to build its own domain KG. Combined with the definition of the general KG, the definition and construction process of the equipment fault diagnosis domain KG are expounded, the specific technical methods of each link of the construction process are summarized, and the application of the equipment fault diagnosis domain KG is explained, and some positive exploration is carried out for the subsequent construction of the equipment fault diagnosis domain KG.
Authors: Feifei Gao, Lin Zhang, Bo Zhang, Wenfeng Wang, Wei Liu, Jingyi Zhang, Han Liu, Shi Qiu, Kai Huang, Mingang Zhang
Keywords: Equipment fault diagnosis, Domain knowledge graph, Ontology building, Data acquisition, Knowledge extraction, Knowledge processing, Knowledge storage
Executive Impact
Key Metrics & Strategic Implications for Enterprise Adoption.
Accuracy Improvement
Efficiency Gain
Scalability Potential
Data Integration
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
94.2%
F1 score for fault entity recognition using BERT-BiLSTM-CRF
Enterprise Process Flow
Data Acquisition
→
Ontology Construction
→
Knowledge Extraction
→
Knowledge Storage
→
KG Application
| Method | Advantages | Disadvantages |
|---|---|---|
| Static (Ontology Engineering) |
|
|
| Semi-automated (Data-driven) |
|
|
| Multi-source Heterogeneous Fusion |
|
|
Application of Knowledge Graph in Fault Diagnosis
The research outlines a practical application of the developed Knowledge Graph for equipment fault diagnosis. For instance, the system can assist in diagnosing issues like 'The air conditioning power supply of the search command vehicle is abnormal, resulting in no heating of the air conditioning.' By leveraging the KG, users can perform intelligent retrieval in natural language, quickly matching related entities and fault phenomena. This leads to multi-level association results. The system automatically extracts knowledge triples from maintenance manuals, fault reports, and expert experiences, dynamically updating the knowledge base. This approach resolves the inefficiency of manual sorting and allows for quicker identification of root causes, avoiding time-consuming disassembly processes. The KG provides intelligent support for the entire life cycle of equipment, enhancing maintenance efficiency and decision-making.
Key Takeaway: KG enables intelligent, rapid, and accurate fault diagnosis, significantly reducing maintenance time and improving equipment support.
Advanced ROI Calculator
Estimate the potential savings and reclaimed hours by implementing AI in your operations.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
AI Implementation Roadmap
A structured approach to integrating knowledge graph technology into your enterprise.
Phase 1: Data Infrastructure Setup
Establish data acquisition pipelines, integrate diverse data sources, and set up the graph database (e.g., Neo4j).
Phase 2: Ontology & Schema Definition
Collaborate with domain experts to define the core ontology for equipment fault diagnosis, including entities, relationships, and attributes.
Phase 3: Knowledge Extraction Engine Development
Develop and fine-tune NLP models (e.g., BERT-BiLSTM-CRF) for automated entity and relation extraction from unstructured and semi-structured texts.
Phase 4: KG Population & Initial Validation
Populate the graph database with extracted knowledge and perform initial validation for consistency and accuracy.
Phase 5: Application Integration & UI Development
Integrate the KG with existing PHM systems, remote maintenance, and IETM manuals. Develop a user-friendly visualization and query interface.
Phase 6: Continuous Learning & Maintenance
Implement mechanisms for dynamic knowledge updates, reasoning, and ongoing model refinement based on new data and feedback.
Ready to Transform Your Enterprise?
Leverage cutting-edge AI to drive efficiency, enhance decision-making, and unlock new opportunities. Our experts are ready to guide you.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat