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BLTTNet: feature fusion based on BiLSTM-Transfomer-TCN for prediction of remaining useful life of aircraft engines. [PDF]
Sci RepYang Y +6 more
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Predicting the remaining useful life of metro pantograph sliding strips using gamma processes and its implications for maintenance scheduling. [PDF]
PLoS OneLiu J, Wu C.
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Research on the Bearing Remaining Useful Life Prediction Method Based on Optimized BiLSTM. [PDF]
Sensors (Basel)Zou Y, Sun W, Xu T, Wang B.
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Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction
2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP), 2022In the current technological context where signals can assist the functionality of the engines in operation and the correct functionality can be monitored.
Eliza Maria Olariu +3 more
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Volume 13: Design, Reliability, Safety, and Risk, 2018
Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially ...
Sangram Patil +5 more
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Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially ...
Sangram Patil +5 more
openaire +2 more sources
Aircraft Engine Remaining Useful Life (RUL) Prediction Using Machine Learning
2024 International Conference on Machine Learning and Applications (ICMLA)Knowing the Remaining Useful Life (RUL) of aircraft engines is of paramount importance in the aviation industry. RUL helps anticipate engine failures beforehand so that airlines can proactively schedule maintenance, optimize resource allocation, and ...
Michael Kimollo, Xudong Liu
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Production Engineering, 2021
Accurate prediction of machine tool wear is an essential part of modern and efficient manufacturing. In recent years, many studies have been carried out using machine learning algorithms, both traditional and deep learning; with the latter ones reporting the highest precisions.
Fabio C. Zegarra +2 more
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Accurate prediction of machine tool wear is an essential part of modern and efficient manufacturing. In recent years, many studies have been carried out using machine learning algorithms, both traditional and deep learning; with the latter ones reporting the highest precisions.
Fabio C. Zegarra +2 more
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Volume 5: 26th Design for Manufacturing and the Life Cycle Conference (DFMLC), 2021
Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of
Mohammad Rubyet Islam, Peter Sandborn
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Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of
Mohammad Rubyet Islam, Peter Sandborn
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IEEE Transactions on Industrial Electronics, 2022
Machine remaining useful life (RUL) prediction is vital in improving the reliability of industrial systems and reducing maintenance cost. Recently, long short-term memory (LSTM) based algorithms have achieved state-of-the-art performance for RUL prediction due to their strong capability of modeling sequential sensory data.
Qing Xu +5 more
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Machine remaining useful life (RUL) prediction is vital in improving the reliability of industrial systems and reducing maintenance cost. Recently, long short-term memory (LSTM) based algorithms have achieved state-of-the-art performance for RUL prediction due to their strong capability of modeling sequential sensory data.
Qing Xu +5 more
openaire +2 more sources