A Continuous Remaining Useful Life Prediction Method With Multistage Attention Convolutional Neural Network and Knowledge Weight Constraint

The rotating machinery is continuously monitored in practical application. However, the historical life-cycle data cannot be always preserved due to the limited storage resource; meanwhile, the on-site computing platform cannot process a large number of monitoring samples. It brings a great challeng...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-10, Vol.PP, p.1-14
Hauptverfasser:	Zhou, Jianghong, Qin, Yi
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Attention mechanism Computational modeling continuous learning (CL) Convolutional neural networks Data models deep learning Degradation Feature extraction Maintenance Monitoring Predictive models remaining useful life (RUL) rotating machinery Vectors
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