Uncertainty-Aware Fault Diagnosis of Rotating Compressors Using Dual-Graph Attention Networks

Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the a...

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Bibliographic Details
Published in:Machines (Basel) Vol. 13; no. 8; p. 673
Main Authors: Lee, Seungjoo, Kim, YoungSeok, Choi, Hyun-Jun, Ji, Bongjun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
Accuracy
Attention
Bayesian analysis
Compressors
Decision trees
Deep learning
digital twin simulation
Digital twins
Energy consumption
Epistemology
Fault detection
Fault diagnosis
Forecasting
Forecasts and trends
Fourier transforms
Machine learning
Mathematical models
Misalignment
Neural networks
Parameter estimation
Parameter identification
Predictive maintenance
Preventive maintenance
Process controls
rotating compressor fault diagnosis
Rotating machinery
Rotor dynamics
Sensors
Simulation methods
Statistical inference
Support vector machines
Uncertainty analysis
vibration-based condition monitoring
Waveforms
Wavelet transforms
New York
United States
ISSN:2075-1702, 2075-1702
Online Access:Get full text
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Summary:Rotating compressors are foundational in various industrial processes, particularly in the oil-and-gas sector, where reliable fault detection is crucial for maintaining operational continuity. While Graph Attention Network (GAT) frameworks are widely available, this study advances the state of the art by introducing a Bayesian GAT method specifically tailored for vibration-based compressor fault diagnosis. The approach integrates domain-specific digital-twin simulations built with Rotordynamic software (1.3.0), and constructs dual adjacency matrices to encode both physically informed and data-driven sensor relationships. Additionally, a hybrid forecasting-and-reconstruction objective enables the model to capture short-term deviations as well as long-term waveform fidelity. Monte Carlo dropout further decomposes prediction uncertainty into aleatoric and epistemic components, providing a more robust and interpretable model. Comparative evaluations against conventional Long Short-Term Memory (LSTM)-based autoencoder and forecasting methods demonstrate that the proposed framework achieves superior fault-detection performance across multiple fault types, including misalignment, bearing failure, and unbalance. Moreover, uncertainty analyses confirm that fault severity correlates with increasing levels of both aleatoric and epistemic uncertainty, reflecting heightened noise and reduced model confidence under more severe conditions. By enhancing GAT fundamentals with a domain-tailored dual-graph strategy, specialized Bayesian inference, and digital-twin data generation, this research delivers a comprehensive and interpretable solution for compressor fault diagnosis, paving the way for more reliable and risk-aware predictive maintenance in complex rotating machinery.
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ISSN:2075-1702
2075-1702
DOI:10.3390/machines13080673