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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Vydané v:	Machines (Basel) Ročník 13; číslo 8; s. 673
Hlavní autori:	Lee, Seungjoo, Kim, YoungSeok, Choi, Hyun-Jun, Ji, Bongjun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.08.2025
Predmet:	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
On-line prístup:	Získať plný text
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