Adaptively Lightweight Spatiotemporal Information-Extraction-Operator-Based DL Method for Aero-Engine RUL Prediction [PDF]
Sensors, 2023 Accurate prediction of machine RUL plays a crucial role in reducing human casualties and economic losses, which is of significance. The ability to handle spatiotemporal information contributes to improving the prediction performance of machine RUL ...
Junren Shi, Jun Gao, Sheng Xiang
doaj +2 more sources
A novel gear RUL prediction method by diffusion model generation health index and attention guided multi-hierarchy LSTM [PDF]
Scientific ReportsGears, as indispensable components of machinery, demand accurate prediction of their Remaining Useful Life (RUL). To enhance the utilization of ordered information within time series data and elevate RUL prediction precision, this study introduces the ...
Xinping Chen
doaj +2 more sources
Research on Remaining Useful Life Prediction of Equipment Based on Digital Twins [PDF]
SensorsRemaining Useful Life (RUL) prediction is a key factor in fault diagnosis, prediction, and health management (PHM) during equipment operation and service.
Jiaju Wu +5 more
doaj +2 more sources
A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM [PDF]
Scientific ReportsAbstract Rolling bearings are pivotal components within rotating mechanical systems, and accurately predicting their remaining service life holds significant practical importance. This paper addresses issues prevalent in common deep learning methods for predicting remaining useful life (RUL), notably inadequate feature extraction and low ...
Qiang Liu +9 more
openaire +4 more sources
A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification [PDF]
SensorsRemaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features.
Lilu Wang +3 more
openaire +4 more sources
Sensors, 2022 The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment.
Haitao Wang +3 more
doaj +1 more source
Energies, 2023 Lithium-ion batteries are a green and environmental energy storage component, which have become the first choice for energy storage due to their high energy density and good cycling performance.
Liyuan Shao +5 more
doaj +1 more source
Stochastic RUL calculation enhanced with TDNN-based IGBT failure modeling [PDF]
, 2016 Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets.
Alghassi, Alireza +2 more
core +1 more source
A belief function theory based approach to combining different representation of uncertainty in prognostics [PDF]
, 2015 International audienceIn this work, we consider two prognostic approaches for the prediction of the remaining useful life (RUL) of degrading equipment.
Baraldi, Piero +2 more
core +4 more sources
A New Model for Remaining Useful Life Prediction Based on NICE and TCN-BiLSTM under Missing Data
Machines, 2022 The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited
Jianfei Zheng +4 more
doaj +1 more source