Enabling generalizable RUL prediction for equipment: a dual-dimensional continual learning approach based on deep Gaussian process. [PDF]
Sci RepXiuli L, Zifu C, Guoxin W, Shuo C.
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Circular-economy residual-value assessment of retired lithium-ion batteries via degradation prediction and game-theoretic subsidies. [PDF]
iScienceLan G, Chen Y, Yao D, Wang J.
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A physics-informed multi-modal transformer framework for predictive maintenance and remaining useful life estimation of electrical submersible pumps. [PDF]
Sci RepKhalili Y, Ahmadi M, Moraveji MK.
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Enhancing Remaining Useful Life (RUL) Prediction through CNN-Based Fusion Architectures
Predictive Maintenance (PdM) enables proactive detection of equipment degradation and reduction of downtime and operation costs. Remaining Useful Life (RUL) estimation is central to PdM using sensor-derived time-series data to predict the time until an equipment fails.
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A Fractional-Derivative Multi-Kernel Adaptive Learning Approach for Remaining Useful Life Prediction of Rotating Machinery. [PDF]
Sensors (Basel)Pan L, Xu J, Peng L, Bi D, Xie Y.
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Elastic Ranking-Driven Time Series Feature Optimization for Aeroengine Remaining Useful Life Prediction. [PDF]
Entropy (Basel)Xin R, Bai S, Fan J, Gao C, Feng X.
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Sensor-Based Channel-Wise Remaining Useful Life Estimation and Multi-Threshold Maintenance Policy for Steel-Cord Conveyor Belts. [PDF]
Sensors (Basel)Błażej R, Jurdziak L, Rzeszowska A.
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Uncertainty-Aware Remaining Useful Life Prediction via Synergizing TCN-Transformer Networks and Fractional Brownian Motion. [PDF]
Entropy (Basel)Geng Y, Yu T, Liu Y, Zhao J.
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A deep learning framework for remaining useful life prediction of turbofan engines with partial sensor failure. [PDF]
PLoS OneTang D.
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