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Temporal-Aware Chain-of-Thought Reasoning for Vibration-Based Pump Fault Diagnosis

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Publicat a:Processes vol. 13, no. 8 (2025), p. 2624-2647
Autor principal: Zeng Jinchao
Altres autors: Li, Zicheng, Zheng Zuopeng, Lin Qizhe
Publicat:
MDPI AG
Matèries:
Accuracy
Deep learning
Wavelet transforms
Artificial neural networks
Real time
Signal processing
Computer applications
Gear pumps
Long short-term memory
Vibration analysis
Machine learning
Fault diagnosis
Maintenance costs
Fourier transforms
Neural networks
Process controls
Reasoning
Support vector machines
Network latency
Industrial applications
Computers
Vibration
Latency
Predictive maintenance
Industrial Internet of Things
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Resum:Industrial pump systems require real-time fault diagnosis for predictive maintenance, but conventional Chain-of-Thought (COT) reasoning faces computational bottlenecks when processing high-frequency vibration data. This paper proposes Vibration-Aware COT (VA-COT), a novel framework that integrates multi-domain feature fusion (time, frequency, time–frequency) with adaptive reasoning depth control. Key innovations involve expert prior-guided dynamic feature selection to optimize edge-device inputs, complexity-aware reasoning chains reducing computational steps by 40–65% through confidence-based early termination, and lightweight deployment on industrial ARM-based single-board computers (SBCs). Evaluated on a 12-class pump fault dataset (5400 samples from centrifugal/gear pumps), VA-COT achieves 93.2% accuracy surpassing standard COT (89.3%) and CNN–LSTM (Convolutional Neural Network-Long Short-Term Memory network) (91.2%), while cutting latency to <1.1 s and memory usage by 65%. Six-month validation at pump manufacturing facilities demonstrated 35% maintenance cost reduction and 98% faster diagnostics versus manual methods, proving its viability for IIoT (Industrial Internet of Things) deployment.
ISSN:2227-9717
DOI:10.3390/pr13082624
Font:Materials Science Database