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TCRSCANet: Harnessing Temporal Convolutions and Recurrent Skip Component for Enhanced RUL Estimation in Mechanical Systems
Estimating the remaining useful life (RUL) of critical industrial assets is of crucial importance for optimizing maintenance strategies, enabling...
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M2BIST-SPNet: RUL prediction for railway signaling electromechanical devices
Railway signaling electromechanical devices (RSEDs) play a pivotal role in the railway industry. Normal wear and tear of these devices occur during...
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RUL prediction for lithium-ion batteries based on variational mode decomposition and hybrid network model
Lithium-ion batteries are widely used in the field of electric vehicles and energy storage due to their superior performance. However, with increased...
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Boosting RUL Prediction Using a Hybrid Deep CNN-BLSTM Architecture
AbstractReliable estimation of remaining useful life (RUL) is a critical challenge in prognostics and health management (PHM), enabling the industry...
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Deep LSTM Enhancement for RUL Prediction Using Gaussian Mixture Models
AbstractThis paper introduces a new deep learning model for Remaining Useful Life (RUL) prediction of complex industrial system components using...
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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion
Brownian motion (BM) has been widely used for degradation modeling and remaining useful life (RUL) prediction, but it is essentially Markovian. This...
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Deep Ensemble Approach for RUL Estimation of Aircraft Engines
Remaining useful life estimation (RUL) is the remaining time until the system failure. Predicting RUL help to schedule the maintenance actions in...
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Remaining useful life (RUL) prediction of internal combustion engine timing belt based on vibration signals and artificial neural network
Timing belt rupture, which can develop quickly and cause severe harm to various engine components, usually occurs unexpectedly and without prior...
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Remaining useful life prediction based on spatiotemporal autoencoder
Remaining Useful Life (RUL) prediction has received a lot of attention as the core of prognostics and health management (PHM) technology. Deep...
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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries
Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods...
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A hybrid-driven remaining useful life prediction method combining asymmetric dual-channel autoencoder and nonlinear Wiener process
Remaining Useful Life (RUL) prediction is an essential aspect of Prognostics and Health Management (PHM), facilitating the assessment of mechanical...
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A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation
Remaining useful life (RUL) prediction is significant for reliability analysis and the reduction of maintenance costs for turbofan engine systems....
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Accurate remaining useful life estimation of lithium-ion batteries in electric vehicles based on a measurable feature-based approach with explainable AI
As Electric Vehicles (EVs) become increasingly prevalent, accurately estimating Lithium-ion Batteries (LIBs) Remaining Useful Life (RUL) is crucial...
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Mechanical element’s remaining useful life prediction using a hybrid approach of CNN and LSTM
For the safety and reliability of the system, Remaining Useful Life (RUL) prediction is considered in many industries. The traditional machine...
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Prediction of fault evolution and remaining useful life for rolling bearings with spalling fatigue using digital twin technology
AbstractQuantifying fault severity is a critical part of rolling bearing health management. There are numerous methods for evaluating the severity of...
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Evolutionary Optimization of Convolutional Extreme Learning Machine for Remaining Useful Life Prediction
Remaining useful life (RUL) prediction is a key enabler for making optimal maintenance strategies. Data-driven approaches, especially employing...
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Aero-engine remaining useful life prediction based on a long-term channel self-attention network
The accurate prediction of remaining useful life (RUL) is conducive to reducing equipment failure rates and maintenance costs. As the long-term...
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MMoE-GAT: A Multi-Gate Mixture-of-Experts Boosted Graph Attention Network for Aircraft Engine Remaining Useful Life Prediction
Accurately estimating remaining useful life (RUL) is critical to reducing unplanned downtime, lowering maintenance costs, and improving safety and...
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QoS-aware edge server placement for collaborative predictive maintenance in industrial internet of things
Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive...
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A New Approach for Remaining Useful Life Estimation Using Deep Learning
AbstractPrognosis and Health Management (PHM) refer specifically to the prediction phase of the future behavior of the system or subsystem, including...
