Enterprise AI Analysis
A Review of Fault Diagnosis Methods: From Traditional Machine Learning to Large Language Model Fusion Paradigm
Fault diagnosis is a core technology ensuring the safe and efficient operation of industrial systems. A paradigm shift has been observed wherein traditional signal analysis has been replaced by intelligent, algorithm-driven approaches. In recent years, large language models, digital twins, and knowledge graphs have been introduced. A new stage of intelligent integration has been reached that is characterized by data-driven methods, knowledge guidance, and physical–virtual fusion. In the present paper, the evolutionary context of fault diagnosis technologies was systematically reviewed, with a focus on the theoretical methods and application practices of traditional machine learning, digital twins, knowledge graphs, and large language models. First, the research background, core objectives, and development history of fault diagnosis were described. Second, the principles, industrial applications, and limitations of supervised and unsupervised learning were analyzed. Third, innovative uses were examined involving physical-virtual mapping in digital twins, knowledge modeling in knowledge graphs, and feature learning in large language models. Subsequently, a multi-dimensional comparison framework was constructed to analyze the performance indicators, applicable scenarios, and collaborative potential of different technologies. Finally, the key challenges faced in the current fault diagnosis field were summarized. These included data quality, model generalization, and knowledge reuse. Future directions driven by the fusion of large language models, digital twins, and knowledge graphs were also outlined. A comprehensive technical map was established for fault diagnosis researchers, as well as an up-to-date reference. Theoretical innovation and engineering deployment of intelligent fault diagnosis are intended to be supported.
Executive Impact Summary
This review provides a systematic analysis of fault diagnosis technologies, highlighting the evolution from traditional machine learning to advanced fusion paradigms involving digital twins, knowledge graphs, and large language models. Key benefits include enhanced accuracy, generalization, and interpretability in complex industrial systems, driving predictive maintenance and significant economic and safety improvements.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Supervised learning methods leverage labeled historical data to train models for fault classification and regression. Key algorithms include SVMs, Random Forests, XGBoost, and ANNs. While effective for large, labeled datasets and stable conditions, they struggle with data scarcity, dynamic operating environments, and novel fault modes due to strong dependence on prior data distributions.
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Reported Accuracy with SVMs
Enterprise Process Flow
Data Preparation
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Feature Extraction
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Algorithm Selection
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Model Training
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Fault Classification
Unsupervised learning aims to detect faults and cluster data without explicit labels, primarily using normal condition data. Methods like K-means, DBSCAN, and Autoencoders identify abnormal behaviors by finding deviations from normal patterns. While beneficial for data-scarce scenarios and unknown fault modes, these methods often have lower classification accuracy and are sensitive to noise.
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UAV Fault Detection Accuracy
| Feature | K-means | DBSCAN | Autoencoder |
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Digital Twins (DTs) create high-fidelity virtual counterparts of physical systems, enabling real-time monitoring, simulation, and predictive analysis. They address data scarcity by generating synthetic fault data under diverse conditions, improving diagnostic accuracy and robustness. Challenges include high modeling costs and ensuring long-term physical-virtual consistency.
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DT-Enhanced Accuracy for Rear Axle Assembly
DT for Wind Turbine Planetary Gears
Digital twin-based diagnosis of wind-turbine planetary gears has been implemented using empirical mode decomposition and an atom-search-optimized SVM. Model parameters were iteratively refined using diagnostic feedback to achieve high accuracy and adapt to complex operating conditions.
- Enhanced accuracy: Achieved high precision by iterative model refinement.
- Adaptive to complex conditions: DTs simulate diverse scenarios for robust model training.
- Reduced physical testing: Virtual environment allows safe and cost-effective fault reproduction.
Knowledge Graphs (KGs) provide structured semantic networks to represent complex fault mechanisms, supporting interpretable reasoning and root-cause localization. They integrate heterogeneous data and expert knowledge, enabling cross-equipment knowledge reuse and transparent diagnostic traceability. Challenges include high-quality graph construction, temporal modeling, and semantic consistency across diverse sources.
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Entity Extraction Accuracy
Enterprise Process Flow
Data Collection
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Entity Extraction
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Relation Extraction
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Ontology Construction
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Knowledge Reasoning
Large Language Models (LLMs) and their multimodal variants are reshaping fault diagnosis by leveraging extensive semantic knowledge and strong reasoning capabilities. They enable cross-task transfer, zero-shot/few-shot diagnosis, and multimodal data fusion (text, logs, sensor signals). LLMs provide unified, scalable infrastructure for fault diagnosis, enhancing accuracy, interpretability, and human-machine collaboration, particularly in knowledge-intensive stages.
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Performance Boost with Multimodal LLM
| Feature | LLM-driven | Traditional Deep Learning |
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Enterprise Process Flow
Multimodal Data Ingestion
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Semantic Alignment
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LLM Fine-tuning
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Fault Identification
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Maintenance Recommendation
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Your AI Implementation Roadmap
Phase 1: Discovery & Assessment
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Phase 2: Pilot Deployment & Data Integration
Implement a pilot AI-driven fault diagnosis system on a selected set of equipment. Integrate existing sensor data, maintenance logs, and domain knowledge. Train initial models with your operational data.
Phase 3: Model Refinement & Knowledge Augmentation
Iteratively refine AI models based on pilot performance feedback. Integrate digital twin simulations for synthetic data generation and enhance knowledge graphs with expert input to improve diagnostic accuracy and interpretability.
Phase 4: Scaled Rollout & Continuous Optimization
Expand the AI system across your enterprise, ensuring seamless integration with existing operational systems. Establish continuous learning loops for model updates, performance monitoring, and adaptive maintenance recommendations.
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