Enterprise AI Analysis
Multi-Source Data Fusion and Multi-Task Physics-Informed Transformer for Power Transformer Fault Diagnosis
Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-based approaches face challenges related to data heterogeneity, limited interpretability, and weak integration of physical mechanisms. To address these issues, this paper proposes a physics-informed enhanced transformer-based framework for power transformer fault diagnosis. A unified temporal representation scheme is developed to integrate heterogeneous monitoring data using Dynamic Time Warping and physics-guided feature projection. Physical priors derived from thermodynamic laws and gas diffusion principles are embedded into the attention mechanism through multi-physics coupling constraints, improving physical consistency and interpretability. In addition, a multi-task diagnostic strategy is adopted to jointly perform fault classification, severity assessment, and fault localization. Experiments on 3000 samples from 76 power transformers demonstrate that the proposed method achieves high diagnostic accuracy and superior robustness under noise and interference, indicating its effectiveness for practical predictive maintenance applications.
Executive Impact Summary
Our analysis of "Multi-Source Data Fusion and Multi-Task Physics-Informed Transformer for Power Transformer Fault Diagnosis" highlights crucial advancements for enterprise predictive maintenance, particularly in critical infrastructure. This research demonstrates how integrating AI with domain-specific physics can unlock unprecedented levels of accuracy and operational resilience.
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Overall Diagnostic Accuracy
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Accuracy Drop (Test vs. Train)
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Accuracy under 20% Data Loss
Key Challenges Addressed
- — Data scarcity and heterogeneity severely constrain model generalizability, especially for fault data.
- — Traditional threshold methods are ill-suited for dynamic environments; data-driven models need substantial annotated data.
- — Disconnection between model interpretability and physical mechanisms, with black-box AI models lacking causal explanations.
- — Insufficient modeling of nonlinear coupling effects, leading to false alarms.
- — Lack of standardization exacerbates system heterogeneity and data alignment issues.
- — High computational costs for multi-modal data fusion and multi-field coupling simulations.
Our Proposed AI Solution
- — A physics-informed enhanced transformer (PI-Transformer) framework deeply integrating physical priors into the attention mechanism.
- — A unified temporal representation scheme using Dynamic Time Warping and physics-guided feature projection to handle data heterogeneity.
- — Multi-physics coupling constraints embedding thermodynamic and gas diffusion principles into the attention mechanism for physical consistency.
- — A multi-task diagnostic strategy performing fault classification, severity assessment, and localization, optimized by curriculum learning.
- — Comprehensive experimental validation demonstrating 89.70% accuracy and superior robustness under noisy conditions.
- — Physics-guided feature projection mechanism and a multi-physics coupled attention calculation method.
- — Multi-task loss function incorporating thermal balance consistency, temporal continuity, and gas ratio constraints.
- — Two-stage curriculum learning approach and a dynamic constraint weight adjustment mechanism.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Feature Engineering
Model Architecture
Training Strategy
The study develops a unified temporal representation scheme, addressing data heterogeneity and varying sampling rates through Dynamic Time Warping (DTW) and physics-guided feature projection. Raw monitoring values are transformed into physically meaningful representations, such as logarithmic transformation for gas concentrations and relative temperature rise ratios, enhancing their suitability for model processing (Section 2.2, 2.3, 3.2).
An innovative physics-informed enhanced transformer (PI-Transformer) is proposed. It employs a hierarchical embedding network for multi-source data fusion, with a spatiotemporal attention encoder for numerical data and deformable convolution for image data. Crucially, physical priors (thermodynamic laws, gas diffusion) are deeply integrated into the attention mechanism via multi-physics coupling constraints (Section 3.1, 3.2, 3.3).
A multi-task diagnostic strategy is adopted for joint fault classification, severity assessment, and localization. This is optimized by a two-stage curriculum learning approach. The first stage involves physics-based pre-training to grasp fundamental physical laws, followed by supervised fine-tuning that balances data fitting and physical constraints with dynamic weight adjustment (Section 3.4).
89.70%
Overall Fault Diagnosis Accuracy achieved by PI-Transformer
Enterprise Process Flow
Physical mechanism and prior knowledge
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Multisource heterogeneous monitoring data
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Data processing and unified representation
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Feature modeling and fusion guided by physics
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Construction and optimization of the PI-Transformer
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Fault diagnosis result output
| Method | Training Set Accuracy | Test Set Accuracy |
|---|---|---|
| FI-Transformer (Proposed) | 89.70% | 86.90% |
| CNN-LSTM | 84.49% | 81.28% |
| LightGBM | 82.23% | 78.96% |
| XGBoost | 81.67% | 76.52% |
| SVM | 79.74% | 70.78% |
| RF | 78.76% | 73.01% |
| LR | 75.25% | 74.10% |
| The FI-Transformer consistently outperforms benchmarks, showing superior generalization and robustness. | ||
Real-World Application & Robustness Testing
The PI-Transformer was validated on 3000 state samples from 76 oil-immersed transformers, covering diverse operational states and fault patterns in an urban rail transit system (Section 4.1). It achieved a prediction accuracy of 83.2% in continuous monitoring scenarios, with a relative decline of only 4.9% compared to 5.9–14.0% for other classifiers (Figure 5, Section 4.3.2). Under simulated communication interruptions (20% sample loss), it maintained a high accuracy of 82.0%, with a degradation of 7.1%, outperforming other algorithms (9.2–14.4% decline) (Figure 6, Section 4.3.2). This demonstrates strong adaptability to complex operating environments and resilience against data loss, offering reliable support for predictive maintenance.
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